SharkTank India (Season 1,2,3) Exploratory Data Analysis (EDA) 🦈

¶

In [62]:
from IPython.display import IFrame
import datetime
print("Notebook was last executed on:", datetime.date.today().strftime("%Y-%b-%d"), "with Python version")
!python --version
Notebook was last executed on: 2024-Mar-25 with Python version
Python 3.11.5
In [63]:
# Source: Wikipedia
IFrame('https://upload.wikimedia.org/wikipedia/en/2/2f/Shark_Tank_India.jpg', width=330, height=330)
Out[63]:

⚒️ Importing Required Python Libraries¶

In [64]:
import warnings
warnings.filterwarnings('ignore')

import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)

import matplotlib.pyplot as plt
import seaborn as sns
from babel.numbers import format_currency
from wordcloud import WordCloud, STOPWORDS
import geopandas as gpd
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_dark"
pio.renderers.default = 'notebook'

⏳ Check and import dataset¶

In [4]:
shark_tank = pd.read_csv('Shark Tank India.csv', encoding = "ISO-8859-1")
 
nRow, nCol = shark_tank.shape
print(f'\nThere are {nRow} rows and {nCol} columns in the dataset')
total 124
-rw-r--r-- 1 nobody nogroup 124427 Mar 16 07:41 'Shark Tank India.csv'

There are 441 rows and 78 columns in the dataset
In [65]:
shark_tank = pd.read_csv('Shark Tank India.csv',encoding = "ISO-8859-1")

nRow, nCol = shark_tank.shape
print(f'\nThere are {nRow} rows and {nCol} columns in the dataset')
There are 320 rows and 64 columns in the dataset

💵 Exploratory Data Analysis (EDA)¶

In [68]:
# Word cloud based on episode titles
text = " Shark Tank India ".join(cat for cat in shark_tank.loc[shark_tank['Episode Title'].notnull()]['Episode Title'])
stop_words = list(STOPWORDS) + ["Ka", "Ki", "Ke", "Ko", "Se", "Hai", "Ek"]
wordcloud = WordCloud(width=2000, height=1500, stopwords=stop_words, background_color='white', colormap='Reds', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(25,20))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
In [6]:
shark_tank.head(8)
Out[6]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present
0 1 BluePineFoods 1 1 20-Dec-21 4-Feb-22 20-Dec-21 Badlegi Business Ki Tasveer Rannvijay Singh Food Frozen Momos https://bluepinefoods.com/ 2016.0 3 2.0 1.0 NaN 0.0 Middle Delhi Delhi 95.0 8.0 NaN NaN ... 25.0 5.33 NaN NaN NaN NaN NaN NaN NaN 25.0 5.33 NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0 NaN
1 1 BoozScooters 1 2 20-Dec-21 4-Feb-22 20-Dec-21 Badlegi Business Ki Tasveer Rannvijay Singh Vehicles/Electrical Vehicles Renting e-bike for mobility in private spaces https://www.boozup.net/ 2017.0 1 1.0 NaN NaN 0.0 Young Ahmedabad Gujarat 4.0 0.4 NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 20.0 25.00 NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0 NaN
2 1 HeartUpMySleeves 1 3 20-Dec-21 4-Feb-22 20-Dec-21 Badlegi Business Ki Tasveer Rannvijay Singh Beauty/Fashion Detachable Sleeves https://heartupmysleeves.com/ 2021.0 1 NaN 1.0 NaN 0.0 Young Delhi Delhi NaN 2.0 NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0 NaN
3 1 TagzFoods 2 4 20-Dec-21 4-Feb-22 21-Dec-21 Insaan, Ideas Aur Sapne Rannvijay Singh Food Healthy Potato Chips Snacks https://tagzfoods.com/ 2019.0 2 2.0 NaN NaN 0.0 Middle Bangalore Karnataka 700.0 NaN 48.0 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 70.0 2.75 NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0 NaN
4 1 HeadAndHeart 2 5 20-Dec-21 4-Feb-22 21-Dec-21 Insaan, Ideas Aur Sapne Rannvijay Singh Education Brain Development Course https://thehnh.in/ 2015.0 4 1.0 3.0 NaN 1.0 Middle Patiala Punjab 30.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0 NaN
5 1 Agritourism 2 6 20-Dec-21 4-Feb-22 21-Dec-21 Insaan, Ideas Aur Sapne Rannvijay Singh Agriculture Tourism https://www.agritourism.in/ 2005.0 2 1.0 1.0 NaN 1.0 Middle Baramati Maharashtra 79.0 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0 NaN
6 1 qZenseLabs 3 7 20-Dec-21 4-Feb-22 22-Dec-21 Aam Aadmi Ke Business Ideas Rannvijay Singh Food Food Freshness Detector https://www.qzense.com/ 2020.0 2 NaN 2.0 NaN 0.0 Middle Delhi,Mohali Delhi,Punjab 25.0 15.0 NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0 NaN
7 1 Peeschute 3 8 20-Dec-21 4-Feb-22 22-Dec-21 Aam Aadmi Ke Business Ideas Rannvijay Singh Beauty/Fashion Disposable Urine Bag https://www.peeschute.com/ 2019.0 1 1.0 NaN NaN 0.0 Young Jalna Maharashtra 100.0 NaN NaN NaN ... 75.0 6.00 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0 NaN

8 rows × 78 columns

In [7]:
shark_tank.tail(10).T
Out[7]:
431 432 433 434 435 436 437 438 439 440
Season Number 3 3 3 3 3 3 3 3 3 3
Startup Name Myracle.io Cup-ji AToddlerThing FlexifyMe Dharaksha iDreamCareer RockPaperRum Fit&Flex Sukham Smotect
Episode Number 37 38 38 38 39 39 39 40 40 40
Pitch Number 432 433 434 435 436 437 438 439 440 441
Season Start 22-Jan-24 22-Jan-24 22-Jan-24 22-Jan-24 22-Jan-24 22-Jan-24 22-Jan-24 22-Jan-24 22-Jan-24 22-Jan-24
... ... ... ... ... ... ... ... ... ... ...
Aman Present 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
Peyush Present NaN NaN NaN NaN 1.0 1.0 1.0 NaN NaN NaN
Amit Present 1.0 1.0 1.0 1.0 NaN NaN NaN NaN NaN NaN
Ashneer Present NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Guest Present 1.0 NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 1.0

78 rows × 10 columns

In [8]:
shark_tank['Season Number'] = shark_tank['Season Number'].astype(pd.Int32Dtype())
shark_tank['Episode Number'] = shark_tank['Episode Number'].astype(pd.Int32Dtype())
shark_tank['Pitch Number'] = shark_tank['Pitch Number'].astype(pd.Int32Dtype())

shark_tank['Number of Presenters'] = shark_tank['Number of Presenters'].astype(pd.Int32Dtype())
shark_tank['Male Presenters'] = shark_tank['Male Presenters'].astype(pd.Int32Dtype())
shark_tank['Female Presenters'] = shark_tank['Female Presenters'].astype(pd.Int32Dtype())
shark_tank['Transgender Presenters'] = shark_tank['Transgender Presenters'].astype(pd.Int32Dtype())
shark_tank['Couple Presenters'] = shark_tank['Couple Presenters'].astype(pd.Int32Dtype())

shark_tank['Gross Margin'] = shark_tank['Gross Margin'].astype(pd.Int32Dtype())
shark_tank['Net Margin'] = shark_tank['Net Margin'].astype(pd.Int32Dtype())

shark_tank['Started in'] = shark_tank['Started in'].astype(pd.Int32Dtype())
shark_tank['Yearly Revenue'] = shark_tank['Yearly Revenue'].astype(pd.Int32Dtype())

shark_tank['Received Offer'] = shark_tank['Received Offer'].astype(pd.Int32Dtype())
shark_tank['Accepted Offer'] = shark_tank['Accepted Offer'].astype(pd.Int32Dtype())
In [9]:
shark_tank.sample(10).style.set_properties(**{"background-color": "#2a9d8f","color":"white","border": "1px solid black", 'font-size': '10pt'})
Out[9]:
  Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin EBITDA Cash Burn SKUs Has Patents Bootstrapped Original Ask Amount Original Offered Equity Valuation Requested Received Offer Accepted Offer Total Deal Amount Total Deal Equity Total Deal Debt Debt Interest Deal Valuation Number of Sharks in Deal Deal Has Conditions Royalty Deal Advisory Shares Equity Namita Investment Amount Namita Investment Equity Namita Debt Amount Vineeta Investment Amount Vineeta Investment Equity Vineeta Debt Amount Anupam Investment Amount Anupam Investment Equity Anupam Debt Amount Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present
136 1 Gizmoswala 0 137 20-Dec-21 4-Feb-22 nan Unseen Rannvijay Singh Entertainment Sex toys and games https://www.gizmoswala.com/ 2020 3 2 1 0 Middle Mumbai Maharashtra 7.000000 40 nan nan nan nan nan 75.000000 5.000000 1500.000000 0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
227 2 SharmaJiKiAata 25 228 2-Jan-23 10-Mar-23 3-Feb-23 Badhta India Rahul Dua Food Freshly milled atta https://sharmajikaaata.com/ 2019 2 1 1 0 Middle Pune Maharashtra nan 63 38 nan nan nan nan nan 40.000000 10.000000 400.000000 1 1 40.000000 20.000000 nan nan 200.000000 1.000000 nan nan nan nan nan nan nan nan nan 40.000000 20.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan 1.000000 1.000000 1.000000 1.000000 nan 1.000000 nan nan
299 2 ZSportsTech 46 300 2-Jan-23 10-Mar-23 6-Mar-23 Different Colours Of Entrepreneurship Rahul Dua Sports Cricket Sport Shop https://www.zsportstech.com/ 2 2 0 Middle Mumbai Maharashtra 31 nan nan nan nan nan nan 60.000000 2.000000 3000.000000 0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan 1.000000 1.000000 1.000000 1.000000 1.000000 nan nan nan
7 1 Peeschute 3 8 20-Dec-21 4-Feb-22 22-Dec-21 Aam Aadmi Ke Business Ideas Rannvijay Singh Beauty/Fashion Disposable Urine Bag https://www.peeschute.com/ 2019 1 1 0 Young Jalna Maharashtra 100 nan nan nan 2.000000 nan nan 75.000000 4.000000 1875.000000 1 1 75.000000 6.000000 nan nan 1250.000000 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan 75.000000 6.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan 1.000000 1.000000 1.000000 1.000000 nan nan 1.000000 nan
428 3 DesignTemplate 36 429 22-Jan-24 nan 11-Mar-24 Designing Dreams Rahul Dua Technology/Software Online Design Marketplace https://designtemplate.io/ 1 1 0 Middle Bid Maharashtra 160 16.000000 nan nan nan nan nan 100.000000 2.500000 4000.000000 1 1 100.000000 10.000000 nan nan 1000.000000 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan 100.000000 10.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan Ritesh Aggarwal,Radhika Gupta nan 1.000000 nan 1.000000 1.000000 nan nan 2.000000
288 2 WTF 43 289 2-Jan-23 10-Mar-23 1-Mar-23 Creating Value Through Ideas Rahul Dua Food Where's The Food nan 1 1 0 Middle Kolkata West Bengal nan nan nan nan nan nan 75.000000 5.000000 1500.000000 0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan 1.000000 nan 1.000000 1.000000 1.000000 1.000000 nan nan
171 2 TheSimplySalad 7 172 2-Jan-23 10-Mar-23 10-Jan-23 Shaandar Businesses Rahul Dua Food Freshly chopped salads https://simplysalad.com/ 2 1 1 0 Young Ahmedabad Gujarat nan nan nan nan nan nan 30.000000 10.000000 300.000000 1 1 30.000000 10.000000 nan nan 300.000000 2.000000 nan nan nan nan nan nan 15.000000 5.000000 nan nan nan nan 15.000000 5.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan 1.000000 1.000000 1.000000 1.000000 1.000000 nan nan nan
425 3 Namakwali 35 426 22-Jan-24 nan 8-Mar-24 Inspiring Women Entrepreneurs Rahul Dua Food Organic Spices https://www.namakwali.com/ 2018 2 1 1 0 Middle nan Uttarakhand 38 3.000000 17 nan nan nan nan nan 50.000000 5.000000 1000.000000 1 1 10.000000 5.000000 40.000000 8.000000 200.000000 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan 10.000000 5.000000 40.000000 nan nan nan nan nan nan nan nan 1.000000 1.000000 1.000000 1.000000 nan 1.000000 nan nan
132 1 Glii 0 133 20-Dec-21 4-Feb-22 nan Unseen Rannvijay Singh Services Dating app for LGBTQ https://www.glii.in/ 2021 4 3 1 0 Middle Noida Uttar Pradesh nan nan nan nan nan nan 40.000000 4.000000 1000.000000 0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
386 3 HouseOfBeautyIndia 22 387 22-Jan-24 nan 20-Feb-24 Impressive Numbers and High Stakes Rahul Dua Beauty/Fashion Skin care Products https://houseofbeautyindia.com/ 2021 1 1 0 Middle Delhi Delhi 40.000000 nan nan nan nan yes 150.000000 5.000000 3000.000000 0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan 1.000000 1.000000 1.000000 1.000000 nan 1.000000 nan nan
In [10]:
shark_tank.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 441 entries, 0 to 440
Data columns (total 78 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Season Number              441 non-null    Int32  
 1   Startup Name               441 non-null    object 
 2   Episode Number             441 non-null    Int32  
 3   Pitch Number               441 non-null    Int32  
 4   Season Start               441 non-null    object 
 5   Season End                 321 non-null    object 
 6   Original Air Date          410 non-null    object 
 7   Episode Title              441 non-null    object 
 8   Anchor                     441 non-null    object 
 9   Industry                   441 non-null    object 
 10  Business Description       441 non-null    object 
 11  Company Website            430 non-null    object 
 12  Started in                 321 non-null    Int32  
 13  Number of Presenters       441 non-null    Int32  
 14  Male Presenters            381 non-null    Int32  
 15  Female Presenters          206 non-null    Int32  
 16  Transgender Presenters     3 non-null      Int32  
 17  Couple Presenters          437 non-null    Int32  
 18  Pitchers Average Age       441 non-null    object 
 19  Pitchers City              437 non-null    object 
 20  Pitchers State             438 non-null    object 
 21  Yearly Revenue             210 non-null    Int32  
 22  Monthly Sales              210 non-null    float64
 23  Gross Margin               117 non-null    Int32  
 24  Net Margin                 64 non-null     Int32  
 25  EBITDA                     16 non-null     float64
 26  Cash Burn                  56 non-null     object 
 27  SKUs                       27 non-null     float64
 28  Has Patents                39 non-null     object 
 29  Bootstrapped               30 non-null     object 
 30  Original Ask Amount        441 non-null    float64
 31  Original Offered Equity    441 non-null    float64
 32  Valuation Requested        441 non-null    float64
 33  Received Offer             441 non-null    Int32  
 34  Accepted Offer             301 non-null    Int32  
 35  Total Deal Amount          248 non-null    float64
 36  Total Deal Equity          248 non-null    float64
 37  Total Deal Debt            53 non-null     float64
 38  Debt Interest              37 non-null     float64
 39  Deal Valuation             247 non-null    float64
 40  Number of Sharks in Deal   248 non-null    float64
 41  Deal Has Conditions        26 non-null     object 
 42  Royalty Deal               14 non-null     float64
 43  Advisory Shares Equity     3 non-null      float64
 44  Namita Investment Amount   81 non-null     float64
 45  Namita Investment Equity   81 non-null     float64
 46  Namita Debt Amount         14 non-null     float64
 47  Vineeta Investment Amount  63 non-null     float64
 48  Vineeta Investment Equity  63 non-null     float64
 49  Vineeta Debt Amount        11 non-null     float64
 50  Anupam Investment Amount   70 non-null     float64
 51  Anupam Investment Equity   70 non-null     float64
 52  Anupam Debt Amount         7 non-null      float64
 53  Aman Investment Amount     101 non-null    float64
 54  Aman Investment Equity     101 non-null    float64
 55  Aman Debt Amount           14 non-null     float64
 56  Peyush Investment Amount   87 non-null     float64
 57  Peyush Investment Equity   87 non-null     float64
 58  Peyush Debt Amount         11 non-null     float64
 59  Amit Investment Amount     33 non-null     float64
 60  Amit Investment Equity     33 non-null     float64
 61  Amit Debt Amount           6 non-null      float64
 62  Ashneer Investment Amount  21 non-null     float64
 63  Ashneer Investment Equity  21 non-null     float64
 64  Ashneer Debt Amount        2 non-null      float64
 65  Guest Investment Amount    37 non-null     float64
 66  Guest Investment Equity    37 non-null     float64
 67  Guest Debt Amount          6 non-null      float64
 68  Invested Guest Name        37 non-null     object 
 69  All Guest Names            116 non-null    object 
 70  Namita Present             361 non-null    float64
 71  Vineeta Present            287 non-null    float64
 72  Anupam Present             390 non-null    float64
 73  Aman Present               383 non-null    float64
 74  Peyush Present             295 non-null    float64
 75  Amit Present               128 non-null    float64
 76  Ashneer Present            99 non-null     float64
 77  Guest Present              116 non-null    float64
dtypes: Int32(14), float64(46), object(18)
memory usage: 250.8+ KB
In [11]:
shark_tank.describe().T.round(2).style.background_gradient(cmap = 'Oranges')
Out[11]:
  count mean std min 25% 50% 75% max
Season Number 441.000000 1.927438 0.782882 1.000000 1.000000 2.000000 3.000000 3.000000
Episode Number 441.000000 21.215420 13.919291 0.000000 9.000000 21.000000 32.000000 51.000000
Pitch Number 441.000000 221.000000 127.449990 1.000000 111.000000 221.000000 331.000000 441.000000
Started in 321.000000 2018.791277 2.814591 1998.000000 2018.000000 2019.000000 2021.000000 2023.000000
Number of Presenters 441.000000 2.029478 0.829316 1.000000 1.000000 2.000000 2.000000 6.000000
Male Presenters 381.000000 1.695538 0.831426 1.000000 1.000000 2.000000 2.000000 6.000000
Female Presenters 206.000000 1.194175 0.420412 1.000000 1.000000 1.000000 1.000000 3.000000
Transgender Presenters 3.000000 1.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000
Couple Presenters 437.000000 0.180778 0.385275 0.000000 0.000000 0.000000 0.000000 1.000000
Yearly Revenue 210.000000 596.214286 1601.166969 0.000000 75.000000 170.000000 476.000000 18700.000000
Monthly Sales 210.000000 71.174200 259.176637 0.000000 5.625000 20.000000 59.500000 3500.000000
Gross Margin 117.000000 54.547009 21.149099 3.000000 40.000000 55.000000 69.000000 150.000000
Net Margin 64.000000 21.218750 12.547647 1.000000 10.750000 20.000000 30.000000 55.000000
EBITDA 16.000000 11.531250 12.898926 -20.000000 5.000000 10.500000 18.250000 35.000000
SKUs 27.000000 342.185185 1157.611169 1.000000 9.000000 25.000000 110.000000 6000.000000
Original Ask Amount 441.000000 148.969872 1426.542757 0.000000 50.000000 70.000000 100.000000 30000.000000
Original Offered Equity 441.000000 3.789796 3.693267 0.200000 1.000000 2.500000 5.000000 30.000000
Valuation Requested 441.000000 5357.185214 9191.269411 0.000000 1000.000000 2600.000000 6000.000000 120000.000000
Received Offer 441.000000 0.682540 0.466017 0.000000 0.000000 1.000000 1.000000 1.000000
Accepted Offer 301.000000 0.823920 0.381522 0.000000 1.000000 1.000000 1.000000 1.000000
Total Deal Amount 248.000000 66.083119 43.601659 0.000000 40.000000 58.300000 90.000000 300.000000
Total Deal Equity 248.000000 8.531532 9.637837 0.500000 2.500000 5.000000 10.000000 75.000000
Total Deal Debt 53.000000 46.622642 27.182592 20.000000 25.000000 40.000000 50.000000 150.000000
Debt Interest 37.000000 10.432432 3.586553 0.000000 10.000000 10.000000 12.000000 18.000000
Deal Valuation 247.000000 2299.296411 3535.721814 0.000000 434.500000 1000.000000 2500.000000 25000.000000
Number of Sharks in Deal 248.000000 2.004032 1.143537 1.000000 1.000000 2.000000 3.000000 5.000000
Royalty Deal 14.000000 1.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000
Advisory Shares Equity 3.000000 1.533333 0.950438 0.600000 1.050000 1.500000 2.000000 2.500000
Namita Investment Amount 81.000000 32.911608 20.897607 0.000016 20.000000 28.300000 50.000000 100.000000
Namita Investment Equity 81.000000 4.005909 5.161855 0.200000 1.000000 2.080000 5.000000 25.000000
Namita Debt Amount 14.000000 41.082857 21.412774 12.500000 26.250000 40.500000 50.000000 100.000000
Vineeta Investment Amount 63.000000 31.504167 21.360443 0.002500 17.580000 25.000000 40.000000 100.000000
Vineeta Investment Equity 63.000000 4.292286 4.844934 0.200000 1.000000 2.500000 5.000000 25.000000
Vineeta Debt Amount 11.000000 24.923636 14.011572 12.500000 13.750000 20.000000 30.000000 50.000000
Anupam Investment Amount 70.000000 29.659325 21.892187 0.000000 17.500000 25.000000 40.000000 100.000000
Anupam Investment Equity 70.000000 4.816257 5.385230 0.166000 1.037500 2.500000 6.495000 25.000000
Anupam Debt Amount 7.000000 27.142857 16.100503 12.500000 16.250000 20.000000 37.500000 50.000000
Aman Investment Amount 101.000000 34.612599 24.824278 0.000000 17.500000 30.000000 50.000000 150.000000
Aman Investment Equity 101.000000 3.206758 4.518018 0.166000 1.000000 2.000000 4.000000 40.000000
Aman Debt Amount 14.000000 39.665714 18.308146 16.660000 26.250000 37.500000 47.915000 80.000000
Peyush Investment Amount 87.000000 34.602101 30.408193 0.000000 20.000000 28.000000 45.000000 250.000000
Peyush Investment Equity 87.000000 5.683115 10.959026 0.166000 1.000000 2.000000 5.000000 75.000000
Peyush Debt Amount 11.000000 31.090909 15.332675 10.000000 23.500000 25.000000 40.000000 60.000000
Amit Investment Amount 33.000000 36.193939 27.073697 3.500000 15.000000 25.000000 50.000000 100.000000
Amit Investment Equity 33.000000 4.497170 4.775754 0.330000 1.500000 3.000000 5.000000 20.000000
Amit Debt Amount 6.000000 37.500000 18.641352 10.000000 28.750000 40.000000 43.750000 65.000000
Ashneer Investment Amount 21.000000 25.682381 16.860620 1.000000 15.000000 20.000000 30.000000 70.000000
Ashneer Investment Equity 21.000000 4.440000 5.065662 1.000000 2.000000 3.000000 5.000000 25.000000
Ashneer Debt Amount 2.000000 57.000000 59.396970 15.000000 36.000000 57.000000 78.000000 99.000000
Guest Investment Amount 37.000000 38.423047 37.483512 0.000253 20.000000 30.000000 40.500000 200.000000
Guest Investment Equity 37.000000 3.343108 3.811857 0.200000 1.000000 2.330000 4.000000 17.500000
Guest Debt Amount 6.000000 32.553333 26.097530 12.500000 17.500000 25.000000 32.125000 83.320000
Namita Present 361.000000 1.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000
Vineeta Present 287.000000 1.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000
Anupam Present 390.000000 1.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000
Aman Present 383.000000 1.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000
Peyush Present 295.000000 1.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000
Amit Present 128.000000 1.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000
Ashneer Present 99.000000 1.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000
Guest Present 116.000000 1.155172 0.363640 1.000000 1.000000 1.000000 1.000000 2.000000
In [12]:
# Unique values in each column
for col in shark_tank.columns:
    print("Number of unique values in", col, "-", shark_tank[col].nunique())
Number of unique values in Season Number - 3
Number of unique values in Startup Name - 441
Number of unique values in Episode Number - 52
Number of unique values in Pitch Number - 441
Number of unique values in Season Start - 3
Number of unique values in Season End - 2
Number of unique values in Original Air Date - 125
Number of unique values in Episode Title - 126
Number of unique values in Anchor - 2
Number of unique values in Industry - 16
Number of unique values in Business Description - 438
Number of unique values in Company Website - 430
Number of unique values in Started in - 16
Number of unique values in Number of Presenters - 6
Number of unique values in Male Presenters - 6
Number of unique values in Female Presenters - 3
Number of unique values in Transgender Presenters - 1
Number of unique values in Couple Presenters - 2
Number of unique values in Pitchers Average Age - 3
Number of unique values in Pitchers City - 104
Number of unique values in Pitchers State - 42
Number of unique values in Yearly Revenue - 119
Number of unique values in Monthly Sales - 99
Number of unique values in Gross Margin - 42
Number of unique values in Net Margin - 30
Number of unique values in EBITDA - 14
Number of unique values in Cash Burn - 1
Number of unique values in SKUs - 23
Number of unique values in Has Patents - 2
Number of unique values in Bootstrapped - 2
Number of unique values in Original Ask Amount - 48
Number of unique values in Original Offered Equity - 31
Number of unique values in Valuation Requested - 97
Number of unique values in Received Offer - 2
Number of unique values in Accepted Offer - 2
Number of unique values in Total Deal Amount - 39
Number of unique values in Total Deal Equity - 49
Number of unique values in Total Deal Debt - 18
Number of unique values in Debt Interest - 8
Number of unique values in Deal Valuation - 93
Number of unique values in Number of Sharks in Deal - 5
Number of unique values in Deal Has Conditions - 1
Number of unique values in Royalty Deal - 1
Number of unique values in Advisory Shares Equity - 3
Number of unique values in Namita Investment Amount - 30
Number of unique values in Namita Investment Equity - 33
Number of unique values in Namita Debt Amount - 9
Number of unique values in Vineeta Investment Amount - 24
Number of unique values in Vineeta Investment Equity - 31
Number of unique values in Vineeta Debt Amount - 7
Number of unique values in Anupam Investment Amount - 29
Number of unique values in Anupam Investment Equity - 38
Number of unique values in Anupam Debt Amount - 6
Number of unique values in Aman Investment Amount - 35
Number of unique values in Aman Investment Equity - 40
Number of unique values in Aman Debt Amount - 11
Number of unique values in Peyush Investment Amount - 29
Number of unique values in Peyush Investment Equity - 35
Number of unique values in Peyush Debt Amount - 7
Number of unique values in Amit Investment Amount - 19
Number of unique values in Amit Investment Equity - 17
Number of unique values in Amit Debt Amount - 5
Number of unique values in Ashneer Investment Amount - 9
Number of unique values in Ashneer Investment Equity - 14
Number of unique values in Ashneer Debt Amount - 2
Number of unique values in Guest Investment Amount - 22
Number of unique values in Guest Investment Equity - 23
Number of unique values in Guest Debt Amount - 5
Number of unique values in Invested Guest Name - 9
Number of unique values in All Guest Names - 8
Number of unique values in Namita Present - 1
Number of unique values in Vineeta Present - 1
Number of unique values in Anupam Present - 1
Number of unique values in Aman Present - 1
Number of unique values in Peyush Present - 1
Number of unique values in Amit Present - 1
Number of unique values in Ashneer Present - 1
Number of unique values in Guest Present - 2

🏦 Season one/two/three of SHARK TANK INDIA was broadcasted in SonyLiv OTT and Sony TV¶

In [13]:
shark_tank_season1 = shark_tank.loc[shark_tank['Season Number']==1]
shark_tank_season1_without_unseen = shark_tank.loc[(shark_tank['Season Number']==1) & (shark_tank['Episode Number']!=0)]
shark_tank_season2 = shark_tank.loc[shark_tank['Season Number']==2]
shark_tank_season3 = shark_tank.loc[(shark_tank['Season Number']==3) | (shark_tank['Season Number'].isnull())]
In [14]:
# Data set information
print(shark_tank['Season Number'].max(), "total seasons in Indian SharkTank \n")
print(shark_tank['Pitch Number'].max(), "#startups came for pitching \n")
print("In Season 1, in", shark_tank_season1['Episode Number'].max(), "episodes, there were", shark_tank_season1.loc[shark_tank_season1['Episode Number']!=0]['Startup Name'].count(), "(real) pitches and", shark_tank_season1.loc[shark_tank_season1['Episode Number']==0]['Startup Name'].count(),"unseen pitches\n")
print("In Season 2, in", shark_tank_season2['Episode Number'].max(), "episodes, there were", shark_tank_season2.loc[shark_tank_season2['Episode Number']!=0]['Startup Name'].count(), "(real) pitches and", shark_tank_season2.loc[shark_tank_season2['Episode Number']==0]['Startup Name'].count(),"unseen pitch\n")
print("In Season 3, in", shark_tank_season3['Episode Number'].max(), "episodes, there were", shark_tank_season3.loc[shark_tank_season3['Episode Number']!=0]['Startup Name'].count(), "(real) pitches\n")
3 total seasons in Indian SharkTank 

441 #startups came for pitching 

In Season 1, in 36 episodes, there were 122 (real) pitches and 30 unseen pitches

In Season 2, in 51 episodes, there were 168 (real) pitches and 1 unseen pitch

In Season 3, in 40 episodes, there were 120 (real) pitches

In [15]:
# Season-wise number of episodes
pd.pivot_table(shark_tank, values='Episode Number', columns='Season Number', aggfunc='max')
Out[15]:
Season Number 1 2 3
Episode Number 36 51 40
In [16]:
# There were 2 to 4 pitches, in each episode
print(shark_tank.loc[shark_tank['Episode Number']!=0][['Season Number','Episode Number']].value_counts().sort_values(ascending=True).unique())
[2 3 4]
In [73]:
import pandas as pd
import matplotlib.pyplot as plt

# Load the data
data = pd.read_csv("shark Tank India.csv")

# Group by industry and count occurrences
industry_counts = data['Industry'].value_counts()

# Plot the graph
plt.figure(figsize=(10, 6))
industry_counts.plot(kind='bar', color='skyblue')
plt.title('Types of Industries Came for Investment in Shark Tank India')
plt.xlabel('Industry')
plt.ylabel('Number of Appearances')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()
In [78]:
import pandas as pd
import matplotlib.pyplot as plt

# Load the CSV data into a DataFrame
df = pd.read_csv('shark Tank India.csv')

# Assuming the CSV file has a column named 'Industry' which contains types of industries
# You can adjust the column name according to your CSV structure

# Count the occurrences of each industry
industry_counts = df['Industry'].value_counts()

# Plotting
plt.figure(figsize=(10, 6))
industry_counts.plot(kind='bar', color='skyblue')
plt.title('Types of Industries in Shark Tank India (Latest Season)')
plt.xlabel('Industry')
plt.ylabel('Number of Investments')
plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better visibility
plt.tight_layout()  # Adjust layout to prevent clipping of labels
plt.show()
In [79]:
# Gender wise
print("Total pitchers -", int(shark_tank['Number of Presenters'].sum()), "\n")
print("")
print("Total male pitchers -", int(shark_tank['Male Presenters'].sum()), "\n")
print("Total female pitchers -", int(shark_tank['Female Presenters'].sum()), "\n")
print("Total transgender pitchers -", int(shark_tank['Transgender Presenters'].sum()), "\n")
print("")
print("COVID entrepreneurs/startups - ", shark_tank.loc[shark_tank['Started in']==2020]['Startup Name'].count(), sep='')
Total pitchers - 665 


Total male pitchers - 484 

Total female pitchers - 178 

Total transgender pitchers - 3 


COVID entrepreneurs/startups - 31
In [80]:
print("Male entrepreneurs percentage - ", round(shark_tank['Male Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), "%\n", sep='')
print("Female entrepreneurs percentage - ", round(shark_tank['Female Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), "%\n", sep='')
print("Transgender entrepreneurs percentage - ", round(shark_tank['Transgender Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), "%\n", sep='')
print("Couple entrepreneurs percentage - ", round(shark_tank.loc[shark_tank['Couple Presenters']==1]['Couple Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 0), "%\n", sep='')
print("")

fig = plt.figure(figsize =(10, 7))
plt.title("Pitchers Gender wise percentage")
plt.pie([round(shark_tank['Male Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), round(shark_tank['Female Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), round(shark_tank['Transgender Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2)], labels = ["Male","Female","Transgender"], autopct='%.1f%%', colors=["lightblue", "pink", "gray"], textprops={'fontsize': 14})
plt.show()
Male entrepreneurs percentage - 72.78%

Female entrepreneurs percentage - 26.77%

Transgender entrepreneurs percentage - 0.45%

Couple entrepreneurs percentage - 9.0%


In [81]:
# Age wise
print(shark_tank['Pitchers Average Age'].value_counts(),"\n")

# In percentage
print(round(shark_tank['Pitchers Average Age'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%', regex=False),"\n")

plt.title("Pitchers Age wise percentage")
shark_tank["Pitchers Average Age"].value_counts().plot(kind='pie', autopct='%.0f%%', cmap='tab20c', fontsize=14)
plt.ylabel('')
Pitchers Average Age
Middle    231
Young      88
Old         1
Name: count, dtype: int64 

Pitchers Average Age
Middle    72%
Young     28%
Old        0%
Name: proportion, dtype: object 

Out[81]:
Text(0, 0.5, '')

💹 68% companies received offers and 32% startups could not convince Sharks to invest¶

In [82]:
# Offers received
print(shark_tank['Received Offer'].value_counts(), "\n")
print(round(shark_tank['Received Offer'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%', regex=False))

plt.figure(figsize = (15,9))
ax1 = plt.subplot(221)
shark_tank["Received Offer"].value_counts().plot(kind='bar', color=["limegreen","crimson"], ec="k")
plt.xlabel("Number of Offers Received / Not Received")
plt.yticks([])
plt.xticks(rotation=0)
for x,y in enumerate(shark_tank["Received Offer"].value_counts()):
    plt.annotate(y, (x,y), fontsize=13, color="blue")
    
ax2 = plt.subplot(222)
shark_tank["Received Offer"].value_counts().plot(kind='pie', autopct='%.0f%%', explode = (0,0.05), colors=["limegreen","crimson"], shadow=True, fontsize=13)
plt.ylabel('')

# 272 companies received offers & 124 startups could not convince #Sharks to invest.
Received Offer
1    216
0    104
Name: count, dtype: int64 

Received Offer
1    68%
0    32%
Name: proportion, dtype: object
Out[82]:
Text(0, 0.5, '')

$ 82% companies accepted offers and 18% startups didn't accept Sharks offer¶

In [83]:
# Offers accepted
print(shark_tank['Accepted Offer'].value_counts(), "\n")
print(round(shark_tank['Accepted Offer'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%', regex=False))

plt.figure(figsize = (15, 9))
ax1 = plt.subplot(221)
shark_tank["Accepted Offer"].value_counts().plot(kind='bar', color=["limegreen","crimson"], ec="k")
plt.xlabel("Number of Offers Accepted / Rejected")
plt.yticks([])
plt.xticks(rotation = 0)
for x,y in enumerate(shark_tank["Accepted Offer"].value_counts()):
    plt.annotate(y, (x,y), fontsize=13, color="blue")
    
ax2 = plt.subplot(222)
shark_tank["Accepted Offer"].value_counts().plot(kind='pie', autopct='%.0f', explode = (0,0.05), colors=["limegreen","crimson"], shadow=True, fontsize=13)
plt.ylabel('')

# 220 companies accepted investments they got & 52 #Startups did not accept Shark's offer.
Accepted Offer
1.0    176
0.0     40
Name: count, dtype: int64 

Accepted Offer
1.0    81%
0.0    19%
Name: proportion, dtype: object
Out[83]:
Text(0, 0.5, '')
In [84]:
# Offers rejected by pitchers/startup companies
print(shark_tank[shark_tank['Accepted Offer']==0]["Startup Name"].count())
shark_tank.loc[shark_tank['Accepted Offer']==0, ["Season Number","Startup Name","Industry","Original Ask Amount","Original Offered Equity"]]
40
Out[84]:
Season Number Startup Name Industry Original Ask Amount Original Offered Equity
6 1 qZenseLabs Food 100.0 0.25
19 1 Torch-it Education 75.0 1.00
21 1 LaKheerDeli Food 50.0 7.50
27 1 KabiraHandmad Food 100.0 5.00
41 1 MorrikoPureFoods Food 100.0 3.00
55 1 IndiaHempandCo Food 50.0 4.00
60 1 KetoIndia Food 150.0 1.25
70 1 Moonshine Food 80.0 0.50
71 1 Falhari Food 50.0 2.00
73 1 UrbanMonkey Beauty/Fashion 100.0 1.00
74 1 GuardianGears Manufacturing 30.0 5.00
81 1 Alpino Food 150.0 2.00
87 1 AlisteTechnologies Technology/Software 60.0 5.00
93 1 PDDFalcon Manufacturing 75.0 3.00
94 1 PlayBoxTV Services 100.0 3.50
104 1 ExperentialEtc Technology/Software 200.0 4.00
106 1 C3Med-Tech Medical/Health 35.0 6.00
113 1 GreenProtein Food 60.0 2.00
116 1 Woloo Technology/Software 50.0 4.00
119 1 FrenchCrown Beauty/Fashion 150.0 0.33
121 1 Devnagri Technology/Software 100.0 1.00
131 1 Scintiglo Medical/Health 75.0 1.00
135 1 UrbanNaps Services 50.0 4.00
138 1 Picsniff Technology/Software 55.0 1.00
149 1 Artment Beauty/Fashion 170.0 2.50
151 1 Eume Beauty/Fashion 150.0 2.00
158 2 ATMOSPHERE Food 75.0 3.00
165 2 Flatheads Beauty/Fashion 75.0 3.00
189 2 Diabexy Food 150.0 1.00
199 2 AvimeeHerbal Beauty/Fashion 280.0 0.50
206 2 PMV Vehicles/Electrical Vehicles 100.0 1.00
212 2 CheeseCake&Co. Food 100.0 2.00
215 2 BeUnic Services 100.0 10.00
229 2 GavinParis Beauty/Fashion 50.0 5.00
233 2 HobbyIndia Furnishing/Household 50.0 3.00
237 2 DesiToys Manufacturing 50.0 3.00
245 2 Tipayi Manufacturing 50.0 10.00
256 2 MidNightAngelsByPC Beauty/Fashion 75.0 6.00
278 2 TwistingScoops Food 100.0 2.50
316 2 GODESi Food 90.0 0.50
In [85]:
# Maximum amount requested
print("Maximum amount requested, by a pitcher - Rs.", round(shark_tank["Original Ask Amount"].max()/100), "crores")
Maximum amount requested, by a pitcher - Rs. 300 crores
In [86]:
# Least amount requested
print("Least amount requested, by a pitcher - Rs.", round(shark_tank["Original Ask Amount"].min()*100000))
Least amount requested, by a pitcher - Rs. 0
In [87]:
# Sum of investment amount asked, in Shark Tank, in India
print("Sum of investment amount asked, by all startup companies, in Indian Shark Tank -", format_currency(shark_tank['Original Ask Amount'].sum()/100, 'INR', locale='en_IN').replace(".00", ""), "crores")
Sum of investment amount asked, by all startup companies, in Indian Shark Tank - ₹540.41 crores
In [88]:
# Amount invested by all sharks, in India SharkTank
print("Amount invested by all sharks, in Shark Tank India -", format_currency(shark_tank['Total Deal Amount'].sum()/100, 'INR', locale='en_IN').replace(".00", ""), "crores")
Amount invested by all sharks, in Shark Tank India - ₹110.06 crores
In [89]:
# Sum of loan/debt amount, in India Shark Tank
print("Sum of loan/debt amount, given by all sharks, in India SharkTank -", format_currency(shark_tank['Total Deal Debt'].sum()/100, 'INR', locale='en_IN').replace(".00", ""), "crores")
Sum of loan/debt amount, given by all sharks, in India SharkTank - ₹18.11 crores
In [90]:
# Top 20 investments (more than 1Cr), as per total investment/deal amount (in lakhs)
print(shark_tank.groupby('Startup Name')['Total Deal Amount'].max().nlargest(20))

tmpdf = shark_tank.sort_values('Total Deal Amount', ascending=False)[0:20]
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Amount', color="Startup Name", title="Highest Investment as per deal amount (in lakhs)", text=tmpdf['Total Deal Amount'])
fig.show()
Startup Name
MeduLance            200.0
Pharmallama          200.0
UnStop               200.0
AasVidyalaya         150.0
Portl                150.0
Snitch               150.0
Stage                150.0
Trunome              150.0
MindPeers            106.0
Annie                105.0
Broomees             100.0
GearHeadMotors       100.0
Geeani               100.0
Get-A-Whey           100.0
HammerLifestyle      100.0
Haqdarshak           100.0
Hoovu                100.0
HumpyA2              100.0
INACAN               100.0
InsuranceSamadhan    100.0
Name: Total Deal Amount, dtype: float64
In [91]:
# Top 20 investments, as per total equity/shares percentage diluted
print(shark_tank.groupby('Startup Name')['Total Deal Equity'].max().nlargest(20))

tmpdf = shark_tank.sort_values('Total Deal Equity', ascending=False)[0:20]
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Equity', color="Startup Name", title="Highest Investment as per Equity percentage", text=tmpdf['Total Deal Equity'].map(int).map(str) + "%")
fig.show()
Startup Name
Sid07Designs          75.00
BoozScooters          50.00
IsakFragrances        50.00
HammerLifestyle       40.00
KGAgrotech            40.00
TheSassBar            35.00
VivalyfInnovations    33.33
GoldSafeSolutions     30.00
HeartUpMySleeves      30.00
JainShikanji          30.00
ColourMeMad-CMM       25.00
CosIQ                 25.00
FindYourKicksIndia    25.00
HoloKitab             25.00
PNT                   25.00
Raasa                 25.00
LOKA                  24.00
TheQuirkyNaari        24.00
WakaoFoods            21.00
Angrakhaa             20.00
Name: Total Deal Equity, dtype: float64
In [92]:
# Startups who sold more than 1/3rd of their company (equity) to Sharks
print(shark_tank.loc[shark_tank['Total Deal Equity'] > 32 ][["Startup Name"]].count())
print(shark_tank.loc[shark_tank['Total Deal Equity'] > 32 ][["Season Number","Startup Name","Total Deal Amount", "Total Deal Equity"]])

tmpdf = shark_tank.loc[shark_tank['Total Deal Equity'] > 32 ].sort_values('Total Deal Equity', ascending=False)
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Equity', color="Startup Name", title="Startups who sold more than 1/3rd of their company", text=tmpdf['Total Deal Equity'].map(int).map(str) + "%")
fig.show()
Startup Name    7
dtype: int64
    Season Number        Startup Name  Total Deal Amount  Total Deal Equity
1               1        BoozScooters               40.0              50.00
23              1  VivalyfInnovations               56.0              33.33
43              1     HammerLifestyle              100.0              40.00
66              1        Sid07Designs               25.0              75.00
76              1          TheSassBar               50.0              35.00
77              1          KGAgrotech               10.0              40.00
82              1      IsakFragrances               50.0              50.00
In [93]:
# Top 20 investments, as per total debt/loan amount
print(shark_tank.groupby('Startup Name')['Total Deal Debt'].max().nlargest(20))

tmpdf = shark_tank.sort_values('Total Deal Debt', ascending=False)[0:20]
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Debt', color="Startup Name", title="Highest Investment as per Debt amount (in lakhs)", text=tmpdf['Total Deal Debt'])
fig.show()
Startup Name
Stage                150.0
WatchoutWearables    100.0
uBreathe             100.0
Otua                  99.0
Wol3D                 70.0
TAC                   69.0
maisha                65.0
Hood                  60.0
iMumz                 60.0
AyuSynk               50.0
DailyDump             50.0
Freebowler            50.0
GROWiT                50.0
LilGoodness           50.0
NamhyaFoods           50.0
NutriCook             50.0
Rubans                50.0
StoreMyGoods          50.0
Aadvik                45.0
VSMani                41.0
Name: Total Deal Debt, dtype: float64
In [94]:
# Startups who got Debt/loan amount
print("Number of startups who got debt/loan amount", shark_tank['Total Deal Debt'].count(),"\n")
shark_tank.loc[shark_tank['Total Deal Debt'] > 0][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity","Total Deal Debt"]]
Number of startups who got debt/loan amount 39 

Out[94]:
Season Number Startup Name Total Deal Amount Total Deal Equity Total Deal Debt
8 1 NOCD 20.0 15.00 30.0
44 1 PNT 25.0 25.00 25.0
46 1 BambooIndia 50.0 3.50 30.0
56 1 Otua 1.0 1.00 99.0
62 1 TheStatePlate 40.0 3.00 25.0
66 1 Sid07Designs 25.0 75.00 22.0
72 1 NamhyaFoods 50.0 10.00 50.0
77 1 KGAgrotech 10.0 40.00 20.0
120 1 StoreMyGoods 50.0 4.00 50.0
156 2 WatchoutWearables 100.0 10.00 100.0
157 2 SoupX 50.0 18.00 25.0
159 2 Stage 150.0 0.60 150.0
163 2 Brandsdaddy 35.0 5.00 35.0
172 2 AyuSynk 50.0 3.50 50.0
180 2 Freebowler 25.0 7.50 50.0
183 2 DailyDump 30.0 4.00 50.0
196 2 VSMani 19.0 1.00 41.0
201 2 ekatra 20.0 20.00 20.0
204 2 licksters 25.0 5.00 25.0
213 2 Dabble 15.0 10.00 35.0
218 2 HoneyVeda 50.0 20.00 25.0
220 2 SwadeshiBlessings 25.0 5.00 25.0
231 2 BlueTea 50.0 3.00 25.0
248 2 Pabiben 10.0 5.00 40.0
249 2 Homestrap 50.0 7.00 20.0
250 2 uBreathe 50.0 5.00 100.0
252 2 iMumz 10.0 1.00 60.0
254 2 Freakins 50.0 2.50 20.0
277 2 Hood 60.0 0.54 60.0
279 2 GROWiT 50.0 1.00 50.0
282 2 Wol3D 80.0 2.00 70.0
290 2 Aadvik 15.0 1.50 45.0
296 2 NutriCook 50.0 10.00 50.0
297 2 Subhag 20.0 1.00 30.0
306 2 Rubans 100.0 1.00 50.0
309 2 LilGoodness 50.0 1.00 50.0
313 2 maisha 10.0 1.00 65.0
317 2 TAC 81.0 1.00 69.0
318 2 Naara-Aaba 50.0 5.00 25.0
In [36]:
# Startups who gave Royalty
print("Number of startups who gave Royalty", shark_tank['Royalty Deal'].count(),"\n")

shark_tank.loc[shark_tank['Royalty Deal'] == 1][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity"]]
Number of startups who gave Royalty 14 

Out[36]:
Season Number Startup Name Total Deal Amount Total Deal Equity
321 3 HonestHome 100.0 3.00
322 3 AdilQadri 100.0 1.00
342 3 Tiggle 50.0 20.00
351 3 GudGum 80.0 10.00
362 3 DecodeAge 100.0 2.25
375 3 YesMadam 150.0 2.00
378 3 PushSports 80.0 4.00
384 3 D'chica 80.0 2.00
385 3 Refit 200.0 1.00
387 3 Artinci 50.0 5.00
397 3 Cosmix 100.0 1.00
401 3 UnclePetersPanCakes 60.0 3.00
410 3 KryzenBiotech 75.0 15.00
433 3 AToddlerThing 40.0 2.00
In [37]:
# Startups who gave Advisory shares
print("Number of startups who gave Advisory shares/equity", shark_tank['Advisory Shares Equity'].count(),"\n")

shark_tank.loc[shark_tank['Advisory Shares Equity'] > 0][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity", "Advisory Shares Equity"]]

# DATA INCOMPLETE
Number of startups who gave Advisory shares/equity 3 

Out[37]:
Season Number Startup Name Total Deal Amount Total Deal Equity Advisory Shares Equity
334 3 AIKavach/Panoplia 100.0 2.50 2.5
341 3 WeHear 250.0 1.00 1.5
349 3 Arata 100.0 1.33 0.6
In [38]:
# Deals with conditions
print("Number of startups who accepted for conditional deals", shark_tank['Deal Has Conditions'].count(),"\n")

shark_tank.loc[shark_tank['Deal Has Conditions'] == 'yes'][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity", "Advisory Shares Equity"]]
Number of startups who accepted for conditional deals 26 

Out[38]:
Season Number Startup Name Total Deal Amount Total Deal Equity Advisory Shares Equity
8 1 NOCD 20.0 15.00 NaN
29 1 Meatyour 30.0 20.00 NaN
32 1 ARRCOATSurfaceTextures 50.0 15.00 NaN
44 1 PNTRobotics 25.0 25.00 NaN
79 1 PawsIndia 50.0 15.00 NaN
82 1 IsakFragrances 50.0 50.00 NaN
105 1 GrowFitter 50.0 2.00 NaN
224 2 Amore 75.0 7.50 NaN
238 2 CloudWorx 40.0 3.20 NaN
242 2 Daryaganj 90.0 1.00 NaN
243 2 DhruvVidyut 0.0 0.50 NaN
254 2 Freakins 50.0 2.50 NaN
264 2 HoloKitab 45.0 25.00 NaN
266 2 Hornback 50.0 2.50 NaN
298 2 SinghStyled 50.0 10.00 NaN
334 3 AIKavach/Panoplia 100.0 2.50 2.5
341 3 WeHear 250.0 1.00 1.5
349 3 Arata 100.0 1.33 0.6
358 3 DaakRoom 36.0 6.00 NaN
379 3 ORBO 100.0 1.00 NaN
387 3 Artinci 50.0 5.00 NaN
393 3 AristaVault 20.0 1.00 NaN
403 3 CandidMen 60.0 5.00 NaN
416 3 MEPACK 7.0 10.00 NaN
436 3 iDreamCareer 60.0 1.00 NaN
440 3 Smotect 50.0 5.00 NaN

💰 Which shark invested most ?¶

In [39]:
# Amount Invested by sharks, in all seasons
Amount = [shark_tank['Ashneer Investment Amount'].sum(), shark_tank['Namita Investment Amount'].sum(), shark_tank['Anupam Investment Amount'].sum(), shark_tank['Vineeta Investment Amount'].sum(),
    shark_tank['Aman Investment Amount'].sum(), shark_tank['Peyush Investment Amount'].sum(), shark_tank['Guest Investment Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush', 'Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount (in lakhs) by Sharks, in all seasons", fontsize=14)
plt.show()

# Aman invested maximum amount, in all seasons - 35 crores
# Ashneer invested minimum amount, in all seasons - 5 crores
In [40]:
# Amount Invested by sharks
# Season 1
Amount = [shark_tank_season1['Ashneer Investment Amount'].sum(), shark_tank_season1['Namita Investment Amount'].sum(), shark_tank_season1['Anupam Investment Amount'].sum(), shark_tank_season1['Vineeta Investment Amount'].sum(),
    shark_tank_season1['Aman Investment Amount'].sum(), shark_tank_season1['Peyush Investment Amount'].sum(), shark_tank_season1['Guest Investment Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount (in lakhs) by Sharks, in Season 1", fontsize=14)
plt.show()

# Season 2
Amount = [shark_tank_season2['Namita Investment Amount'].sum(), shark_tank_season2['Anupam Investment Amount'].sum(), shark_tank_season2['Vineeta Investment Amount'].sum(),
    shark_tank_season2['Aman Investment Amount'].sum(), shark_tank_season2['Peyush Investment Amount'].sum(), shark_tank_season2['Amit Investment Amount'].sum(), shark_tank_season2['Guest Investment Amount'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount (in lakhs) by Sharks, in Season 2", fontsize=14)
plt.show()

# Season 3
Amount = [shark_tank_season3['Namita Investment Amount'].sum(), shark_tank_season3['Anupam Investment Amount'].sum(), shark_tank_season3['Vineeta Investment Amount'].sum(),
    shark_tank_season3['Aman Investment Amount'].sum(), shark_tank_season3['Peyush Investment Amount'].sum(), shark_tank_season3['Amit Investment Amount'].sum(), shark_tank_season3['Guest Investment Amount'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount (in lakhs) by Sharks, in Season 3", fontsize=14)
plt.show()
In [41]:
# Equity received by sharks, in all seasons
Amount = [shark_tank['Ashneer Investment Equity'].sum(), shark_tank['Namita Investment Equity'].sum(), shark_tank['Anupam Investment Equity'].sum(), shark_tank['Vineeta Investment Equity'].sum(),
    shark_tank['Aman Investment Equity'].sum(), shark_tank['Peyush Investment Equity'].sum(), shark_tank['Guest Investment Equity'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush', 'Guests']
df = {'Name':name, 'Total Equity':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='g')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total equity received (in %), by Sharks, in all companies, in all seasons", fontsize=15)
plt.show()

# Peyush got maximum equity of - 494% in different companies, in all seasons
# Ashneer got minimum equity of - 93% in different companies, in all seasons
In [42]:
# Equity received by sharks
# Season 1
Equity = [shark_tank_season1['Ashneer Investment Equity'].sum(), shark_tank_season1['Namita Investment Equity'].sum(), shark_tank_season1['Anupam Investment Equity'].sum(), shark_tank_season1['Vineeta Investment Equity'].sum(),
    shark_tank_season1['Aman Investment Equity'].sum(), shark_tank_season1['Peyush Investment Equity'].sum(), shark_tank_season1['Guest Investment Equity'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total Equity':Equity}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='g')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Equity):
    plt.text(x=index, y =d+1, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Equity received (in %) by Sharks, in all companies, in Season 1", fontsize=14)
plt.show()

# Season 2
Equity = [shark_tank_season2['Namita Investment Equity'].sum(), shark_tank_season2['Anupam Investment Equity'].sum(), shark_tank_season2['Vineeta Investment Equity'].sum(),
    shark_tank_season2['Aman Investment Equity'].sum(), shark_tank_season2['Peyush Investment Equity'].sum(), shark_tank_season2['Amit Investment Equity'].sum(), shark_tank_season2['Guest Investment Equity'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit', 'Guests']
df = {'Name':name, 'Total Equity':Equity}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='g')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Equity):
    plt.text(x=index, y =d+1, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Equity received (in %) by Sharks, in all companies, in Season 2", fontsize=14)
plt.show()

# Season 3
Equity = [shark_tank_season3['Namita Investment Equity'].sum(), shark_tank_season3['Anupam Investment Equity'].sum(), shark_tank_season3['Vineeta Investment Equity'].sum(),
    shark_tank_season3['Aman Investment Equity'].sum(), shark_tank_season3['Peyush Investment Equity'].sum(), shark_tank_season3['Amit Investment Equity'].sum(), shark_tank_season3['Guest Investment Equity'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit', 'Guests']
df = {'Name':name, 'Total Equity':Equity}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='g')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Equity):
    plt.text(x=index, y =d+1, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Equity received (in %) by Sharks, in all companies, in Season 3", fontsize=14)
plt.show()
In [43]:
# Investment based on the  Debt/loaned Amount, in all seasons
Amount = [shark_tank['Ashneer Debt Amount'].sum(), shark_tank['Namita Debt Amount'].sum(), shark_tank['Anupam Debt Amount'].sum(), shark_tank['Vineeta Debt Amount'].sum(),
    shark_tank['Aman Debt Amount'].sum(), shark_tank['Peyush Debt Amount'].sum(), shark_tank['Guest Debt Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush', 'Guests']
df = {'Name':name, 'Total Equity':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='c')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Debt amount (in lakhs) given by Sharks, in all seasons", fontsize=15)
plt.show()

# Namita gave maximum debt amount, in all seasons - 5 crores
# All guests gave minimum debt amount, in all seasons - 0.87 crores
In [44]:
# Investment based on the  Debt/loaned Amount
# Season 1
debt = [shark_tank_season1['Ashneer Debt Amount'].sum(), shark_tank_season1['Namita Debt Amount'].sum(), shark_tank_season1['Anupam Debt Amount'].sum(), shark_tank_season1['Vineeta Debt Amount'].sum(),
    shark_tank_season1['Aman Debt Amount'].sum(), shark_tank_season1['Peyush Debt Amount'].sum(), shark_tank_season1['Guest Debt Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total debt':debt}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total debt'], color='c')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(debt):
    plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Debt amount (in lakhs) given by Sharks, in Season 1", fontsize=14)
plt.show()

# Season 2
debt = [shark_tank_season2['Namita Debt Amount'].sum(), shark_tank_season2['Anupam Debt Amount'].sum(), shark_tank_season2['Vineeta Debt Amount'].sum(),
    shark_tank_season2['Aman Debt Amount'].sum(), shark_tank_season2['Peyush Debt Amount'].sum(), shark_tank_season2['Amit Debt Amount'].sum(), shark_tank_season2['Guest Debt Amount'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit', 'Guests']
df = {'Name':name, 'Total debt':debt}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total debt'], color='c')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(debt):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Debt amount (in lakhs) given by Sharks, in Season 2", fontsize=14)
plt.show()

# Season 3
debt = [shark_tank_season3['Namita Debt Amount'].sum(), shark_tank_season3['Anupam Debt Amount'].sum(), shark_tank_season3['Vineeta Debt Amount'].sum(),
    shark_tank_season3['Aman Debt Amount'].sum(), shark_tank_season3['Peyush Debt Amount'].sum(), shark_tank_season3['Amit Debt Amount'].sum(), shark_tank_season3['Guest Debt Amount'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit', 'Guests']
df = {'Name':name, 'Total debt':debt}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total debt'], color='c')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(debt):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Debt amount (in lakhs) given by Sharks, in Season 3", fontsize=14)
plt.show()

print("Namita gave 20% higher loan in season 2, compared to season 1\nAman started giving more debt/loan in season 3")
Namita gave 20% higher loan in season 2, compared to season 1
Aman started giving more debt/loan in season 3
In [45]:
# Number of companies invested, in all seasons
Amount = [shark_tank['Ashneer Investment Amount'].count(), shark_tank['Namita Investment Amount'].count(), shark_tank['Anupam Investment Amount'].count(), shark_tank['Vineeta Investment Amount'].count(),
    shark_tank['Aman Investment Amount'].count(), shark_tank['Peyush Investment Amount'].count(), shark_tank['Guest Investment Amount'].count()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'], color='pink')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Number of companies invested, in all seasons", fontsize=14)
plt.show()
In [46]:
# Number of companies invested
# Season 1
Amount = [shark_tank_season1['Ashneer Investment Amount'].count(), shark_tank_season1['Namita Investment Amount'].count(), shark_tank_season1['Anupam Investment Amount'].count(), shark_tank_season1['Vineeta Investment Amount'].count(),
    shark_tank_season1['Aman Investment Amount'].count(), shark_tank_season1['Peyush Investment Amount'].count(), shark_tank_season1['Guest Investment Amount'].count()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'], color='pink')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Number of companies invested, in Season 1", fontsize=14)
plt.show()

# Season 2
Amount = [shark_tank_season2['Namita Investment Amount'].count(), shark_tank_season2['Anupam Investment Amount'].count(), shark_tank_season2['Vineeta Investment Amount'].count(),
    shark_tank_season2['Aman Investment Amount'].count(), shark_tank_season2['Peyush Investment Amount'].count(), shark_tank_season2['Amit Investment Amount'].count(), shark_tank_season2['Guest Investment Amount'].count()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'], color='pink')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Number of companies invested, in Season 2", fontsize=14)
plt.show()

# Season 3
Amount = [shark_tank_season3['Namita Investment Amount'].count(), shark_tank_season3['Anupam Investment Amount'].count(), shark_tank_season3['Vineeta Investment Amount'].count(),
    shark_tank_season3['Aman Investment Amount'].count(), shark_tank_season3['Peyush Investment Amount'].count(), shark_tank_season3['Amit Investment Amount'].count(), shark_tank_season3['Guest Investment Amount'].count()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'], color='pink')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Number of companies invested, in Season 3", fontsize=14)
plt.show()
In [47]:
# Word cloud based on Startup Names, in all seasons
text = " Shark Tank India ".join(cat for cat in shark_tank['Startup Name'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=2000, height=1500, stopwords=stop_words, background_color='black', colormap='Set2', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(25,20))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
In [48]:
# Word cloud based on Startup Names, in current/latest season (3rd season)
text = " Shark Tank India ".join(cat for cat in shark_tank_season3['Startup Name'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=1800, height=1300, stopwords=stop_words, background_color='black', colormap='Set3', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(14,14))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()

🔥 Ashneer Grover's Investments¶

In [99]:
print("Total investments by Ashneer", shark_tank[shark_tank['Ashneer Investment Amount']>=0][['Ashneer Investment Amount']].count().to_string()[-2:])
print("Investment amount by Ashneer", round(shark_tank['Ashneer Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Ashneer", round(shark_tank['Ashneer Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Ashneer", round(shark_tank['Ashneer Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Ashneer Investment Amount']>=0][["Startup Name","Industry","Ashneer Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nAshneer industry wise investments\n")
print(shark_tank[shark_tank['Ashneer Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Ashneer Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Ashneer Investment Amount']>=0] [["Startup Name","Ashneer Investment Amount","Ashneer Investment Equity"]].sort_values(by="Ashneer Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Ashneer Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Ashneer 21
Investment amount by Ashneer 5.39 crores
Equity received by Ashneer 93.24 % in different companies
Debt/loan amount by Ashneer 1.14 crores

Company details:
---------------------------------------------------------------------------
      Startup Name                     Industry  Ashneer Investment Amount
     BluePineFoods                         Food                      25.00
      BoozScooters Vehicles/Electrical Vehicles                      20.00
         TagzFoods                         Food                      70.00
     SkippiIcePops                         Food                      20.00
 RaisingSuperstars                    Education                      50.00
       BeyondSnack                         Food                      25.00
      MotionBreeze Vehicles/Electrical Vehicles                      30.00
         EventBeep                    Education                      10.00
     TheYarnBazaar                Manufacturing                      25.00
       BambooIndia                Manufacturing                      25.00
FindYourKicksIndia               Beauty/Fashion                      10.00
      AasVidyalaya                    Education                      50.00
              Otua Vehicles/Electrical Vehicles                       1.00
           WeSTOCK                  Animal/Pets                      15.00
            INACAN                         Food                      20.00
        Get-A-Whey                         Food                      33.33
     HairOriginals               Beauty/Fashion                      20.00
         TweekLabs                       Sports                      20.00
            Proxgy          Technology/Software                      50.00
  NomadFoodProject                         Food                      10.00
      JainShikanji                         Food                      10.00
---------------------------------------------------------------------------

Ashneer industry wise investments

Industry
Food                            8
Vehicles/Electrical Vehicles    3
Education                       3
Manufacturing                   2
Beauty/Fashion                  2
Animal/Pets                     1
Sports                          1
Technology/Software             1
Name: count, dtype: int64

🎆 Namita Thapar's Investments¶

In [100]:
print("Total investments by Namita", shark_tank[shark_tank['Namita Investment Amount']>0][['Namita Investment Amount']].count().to_string()[-2:])
print("Investment amount by Namita", round(shark_tank['Namita Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Namita", round(shark_tank['Namita Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Namita", round(shark_tank['Namita Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Namita Investment Amount']>0][["Startup Name","Industry","Namita Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nNamita industry wise investments\n")
print(shark_tank[shark_tank['Namita Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Namita Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Namita Investment Amount']>0] [["Startup Name","Namita Investment Amount","Namita Investment Equity"]].sort_values(by="Namita Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Namita Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Namita 66
Investment amount by Namita 20.98 crores
Equity received by Namita 301.59 % in different companies
Debt/loan amount by Namita 5.25 crores

Company details:
---------------------------------------------------------------------------
      Startup Name             Industry  Namita Investment Amount
       JhaJiAchaar                 Food                 28.300000
            Bummer       Beauty/Fashion                 37.500000
     SkippiIcePops                 Food                 20.000000
      Menstrupedia            Education                 50.000000
             Altor        Manufacturing                 25.000000
           Nuutjob       Beauty/Fashion                  8.330000
             Farda       Beauty/Fashion                 15.000000
              Auli       Beauty/Fashion                 75.000000
             Annie            Education                 35.000000
   TheRenalProject       Medical/Health                 50.000000
           Cocofit                 Food                  0.000016
       BeyondWater                 Food                 37.500000
FindYourKicksIndia       Beauty/Fashion                 10.000000
      AasVidyalaya            Education                 50.000000
           WeSTOCK          Animal/Pets                 15.000000
            INACAN                 Food                 20.000000
SunfoxTechnologies       Medical/Health                 20.000000
        RarePlanet        Manufacturing                 65.000000
 WattTechnovations       Medical/Health                  0.000253
        WakaoFoods                 Food                 25.000000
       KabaddiAdda               Sports                 40.000000
   ColourMeMad-CMM       Beauty/Fashion                 40.000000
  NomadFoodProject                 Food                 10.000000
          SneaKare       Beauty/Fashion                  7.000000
      StoreMyGoods             Services                 25.000000
    VeryMuchIndian       Beauty/Fashion                 25.000000
             Stage        Entertainment                 50.000000
            Girgit       Beauty/Fashion                 20.000000
       Brandsdaddy        Manufacturing                 35.000000
        Haqdarshak             Services                 33.330000
           AyuSynk       Medical/Health                 50.000000
 AtypicalAdvantage  Technology/Software                 15.000000
         Nestroots Furnishing/Household                 50.000000
        Freebowler               Sports                 25.000000
         DailyDump        Manufacturing                 30.000000
           Janitri       Medical/Health                100.000000
         InsideFPV        Manufacturing                 18.750000
             Pflow       Medical/Health                 30.000000
            VSMani                 Food                 19.000000
        SpiceStory                 Food                 70.000000
            Snitch       Beauty/Fashion                 30.000000
             Portl             Services                 50.000000
          Broomees             Services                 33.330000
           PadCare        Manufacturing                 25.000000
 SwadeshiBlessings Furnishing/Household                 12.500000
            UnStop  Technology/Software                 50.000000
         CloudWorx  Technology/Software                 20.000000
          Mahantam        Manufacturing                  6.000000
         MindPeers       Medical/Health                 17.660000
          DigiQure       Medical/Health                 40.000000
           Pabiben       Beauty/Fashion                 10.000000
          uBreathe        Manufacturing                 50.000000
           Perfora Furnishing/Household                 26.660000
         MeduLance       Medical/Health                 66.660000
         HoloKitab  Technology/Software                 45.000000
           GladFul                 Food                 16.660000
       Pharmallama       Medical/Health                 40.000000
            GROWiT          Agriculture                 25.000000
           funngro  Technology/Software                 25.000000
      LondonBubble                 Food                 75.000000
            Subhag       Medical/Health                 20.000000
  ThePlatedProject             Services                 25.000000
            SoulUp             Services                 50.000000
            Rubans       Beauty/Fashion                 33.330000
     ForeverModest       Beauty/Fashion                  5.000000
         Sahayatha       Medical/Health                 20.000000
---------------------------------------------------------------------------

Namita industry wise investments

Industry
Beauty/Fashion          13
Medical/Health          12
Food                    11
Manufacturing            8
Services                 6
Technology/Software      5
Education                3
Furnishing/Household     3
Sports                   2
Animal/Pets              1
Entertainment            1
Agriculture              1
Name: count, dtype: int64

㊂ Anupam Mittal's Investments¶

In [101]:
print("Total investments by Anupam", shark_tank[shark_tank['Anupam Investment Amount']>=0][['Anupam Investment Amount']].count().to_string()[-2:])
print("Investment amount by Anupam", round(shark_tank['Anupam Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Anupam", round(shark_tank['Anupam Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Anupam", round(shark_tank['Anupam Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*85)
print(shark_tank.loc[shark_tank['Anupam Investment Amount']>=0][["Startup Name","Industry","Anupam Investment Amount"]].to_string(index=False))
print('-'*85)

print("\nAnupam industry wise investments\n")
print(shark_tank[shark_tank['Anupam Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Anupam Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Anupam Investment Amount']>=0] [["Startup Name","Anupam Investment Amount","Anupam Investment Equity"]].sort_values(by="Anupam Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Anupam Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Anupam 51
Investment amount by Anupam 14.51 crores
Equity received by Anupam 305.83 % in different companies
Debt/loan amount by Anupam 0.98 crores

Company details:
-------------------------------------------------------------------------------------
          Startup Name                     Industry  Anupam Investment Amount
      HeartUpMySleeves               Beauty/Fashion                 12.500000
                 CosIQ               Beauty/Fashion                 25.000000
            RevampMoto Vehicles/Electrical Vehicles                 50.000000
         SkippiIcePops                         Food                 20.000000
                Kavach                    Education                  2.500000
    VivalyfInnovations               Medical/Health                 28.000000
              Meatyour                         Food                 10.000000
ARRCOATSurfaceTextures                Manufacturing                 50.000000
                  LOKA          Technology/Software                 13.330000
                 Annie                    Education                 35.000000
            Carragreen                Manufacturing                 25.000000
         TheYarnBazaar                Manufacturing                 25.000000
               Cocofit                         Food                  0.000016
           BambooIndia                Manufacturing                 25.000000
              Let'sTry                         Food                 22.500000
    FindYourKicksIndia               Beauty/Fashion                 10.000000
                INACAN                         Food                 20.000000
        TheQuirkyNaari               Beauty/Fashion                 17.500000
         HairOriginals               Beauty/Fashion                 20.000000
            TheSassBar               Beauty/Fashion                 25.000000
             PawsIndia                  Animal/Pets                 50.000000
    SunfoxTechnologies               Medical/Health                 20.000000
     WattTechnovations               Medical/Health                  0.000253
             TweekLabs                       Sports                 20.000000
          JainShikanji                         Food                 10.000000
                 Dorji                         Food                 10.000000
     WatchoutWearables                  Electronics                 50.000000
             PatilKaki                         Food                 20.000000
               Winston               Beauty/Fashion                 50.000000
                TeaFit             Liquor/Beverages                 12.500000
           Zillionaire               Beauty/Fashion                100.000000
                 Kyari                Manufacturing                 25.500000
               Solinas                     Services                 45.000000
                 Raasa                         Food                 50.000000
                Snitch               Beauty/Fashion                 30.000000
                 Ravel               Beauty/Fashion                 75.000000
             HoneyVeda                         Food                 25.000000
               PadCare                Manufacturing                 25.000000
                Geeani Vehicles/Electrical Vehicles                 33.330000
                 Amore                         Food                 75.000000
        SharmaJiKiAata                         Food                 40.000000
                UnStop          Technology/Software                 50.000000
             CloudWorx          Technology/Software                 20.000000
              Mahantam                Manufacturing                  6.000000
           DhruvVidyut Vehicles/Electrical Vehicles                  0.000000
             Homestrap         Furnishing/Household                 50.000000
           Pharmallama               Medical/Health                 40.000000
               Trunome               Medical/Health                 37.500000
      What'sUpWellness                         Food                 20.000000
         ForeverModest               Beauty/Fashion                  5.000000
             Sahayatha               Medical/Health                 20.000000
-------------------------------------------------------------------------------------

Anupam industry wise investments

Industry
Food                            13
Beauty/Fashion                  11
Manufacturing                    7
Medical/Health                   6
Vehicles/Electrical Vehicles     3
Technology/Software              3
Education                        2
Animal/Pets                      1
Sports                           1
Electronics                      1
Liquor/Beverages                 1
Services                         1
Furnishing/Household             1
Name: count, dtype: int64

🎇 Vineeta Singh's Investments¶

Vineeta's portfolio has 40% Food industry and 23% Beauty/Fashion industry¶

In [102]:
print("Total investments by Vineeta", shark_tank[shark_tank['Vineeta Investment Amount']>0][['Vineeta Investment Amount']].count().to_string()[-2:])
print("Investment amount by Vineeta", round(shark_tank['Vineeta Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Vineeta", round(shark_tank['Vineeta Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Vineeta", round(shark_tank['Vineeta Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Vineeta Investment Amount']>0][["Startup Name","Industry","Vineeta Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nVineeta industry wise investments\n")
print(shark_tank[shark_tank['Vineeta Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Vineeta Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Vineeta Investment Amount']>0] [["Startup Name","Vineeta Investment Amount","Vineeta Investment Equity"]].sort_values(by="Vineeta Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Vineeta Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Vineeta 43
Investment amount by Vineeta 11.71 crores
Equity received by Vineeta 242.3 % in different companies
Debt/loan amount by Vineeta 2.29 crores

Company details:
---------------------------------------------------------------------------
      Startup Name                     Industry  Vineeta Investment Amount
     BluePineFoods                         Food                      25.00
      BoozScooters Vehicles/Electrical Vehicles                      20.00
  HeartUpMySleeves               Beauty/Fashion                      12.50
              NOCD                         Food                      20.00
             CosIQ               Beauty/Fashion                      25.00
       JhaJiAchaar                         Food                      28.30
     SkippiIcePops                         Food                      20.00
        Get-A-Whey                         Food                      33.33
    TheQuirkyNaari               Beauty/Fashion                      17.50
SunfoxTechnologies               Medical/Health                      20.00
           HumpyA2                         Food                      33.33
 GoldSafeSolutions                Manufacturing                      16.66
        WakaoFoods                         Food                      25.00
       KabaddiAdda                       Sports                      40.00
  NomadFoodProject                         Food                      10.00
      JainShikanji                         Food                      10.00
          SneaKare               Beauty/Fashion                       7.00
             Dorji                         Food                      10.00
 WatchoutWearables                  Electronics                      50.00
             SoupX                         Food                      50.00
           Winston               Beauty/Fashion                      50.00
            TeaFit             Liquor/Beverages                      12.50
    TheSimplySalad                         Food                      15.00
          Paradyes               Beauty/Fashion                      32.50
            Snitch               Beauty/Fashion                      30.00
         HoneyVeda                         Food                      25.00
           PadCare                Manufacturing                      25.00
 SwadeshiBlessings         Furnishing/Household                      12.50
               OLL          Technology/Software                      15.00
            Geeani Vehicles/Electrical Vehicles                      33.33
     TheGreenSnack                         Food                     100.00
          Mahantam                Manufacturing                       6.00
         MindPeers               Medical/Health                      17.66
          Freakins               Beauty/Fashion                      50.00
           Perfora         Furnishing/Household                      26.66
           Trunome               Medical/Health                      37.50
  What'sUpWellness                         Food                      20.00
     HealthyMaster                         Food                      50.00
         NutriCook                         Food                      50.00
  ThePlatedProject                     Services                      25.00
            Rubans               Beauty/Fashion                      33.33
     ForeverModest               Beauty/Fashion                       5.00
        Naara-Aaba             Liquor/Beverages                      25.00
---------------------------------------------------------------------------

Vineeta industry wise investments

Industry
Food                            17
Beauty/Fashion                  10
Medical/Health                   3
Manufacturing                    3
Vehicles/Electrical Vehicles     2
Liquor/Beverages                 2
Furnishing/Household             2
Sports                           1
Electronics                      1
Technology/Software              1
Services                         1
Name: count, dtype: int64

🚀 Aman Gupta's Investments¶

In [103]:
print("Total investments by Aman", shark_tank[shark_tank['Aman Investment Amount']>=0][['Aman Investment Amount']].count().to_string()[-2:])
print("Investment amount by Aman", round(shark_tank['Aman Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Aman", round(shark_tank['Aman Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Aman", round(shark_tank['Aman Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Aman Investment Amount']>=0][["Startup Name","Industry","Aman Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nAman industry wise investments\n")
print(shark_tank[shark_tank['Aman Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Aman Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Aman Investment Amount']>=0] [["Startup Name","Aman Investment Amount","Aman Investment Equity"]].sort_values(by="Aman Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Aman Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Aman 73
Investment amount by Aman 25.1 crores
Equity received by Aman 260.93 % in different companies
Debt/loan amount by Aman 3.11 crores

Company details:
---------------------------------------------------------------------------
      Startup Name                     Industry  Aman Investment Amount
     BluePineFoods                         Food               25.000000
         Peeschute               Beauty/Fashion               75.000000
            Bummer               Beauty/Fashion               37.500000
        RevampMoto Vehicles/Electrical Vehicles               50.000000
     SkippiIcePops                         Food               20.000000
 RaisingSuperstars                    Education               50.000000
            Kavach                    Education                2.500000
       BeyondSnack                         Food               25.000000
             Altor                Manufacturing               25.000000
             Ariro                Manufacturing               25.000000
           Nuutjob               Beauty/Fashion                8.330000
          Meatyour                         Food               10.000000
         EventBeep                    Education               10.000000
             Farda               Beauty/Fashion               15.000000
              LOKA          Technology/Software               13.330000
     TheYarnBazaar                Manufacturing               25.000000
   TheRenalProject               Medical/Health               50.000000
   HammerLifestyle                  Electronics              100.000000
           Cocofit                         Food                0.000016
       BeyondWater                         Food               37.500000
          Let'sTry                         Food               22.500000
FindYourKicksIndia               Beauty/Fashion               10.000000
           WeSTOCK                  Animal/Pets               15.000000
            INACAN                         Food               20.000000
        Get-A-Whey                         Food               33.330000
       NamhyaFoods                         Food               50.000000
          AyuRythm               Medical/Health               75.000000
        GrowFitter          Technology/Software               50.000000
      JainShikanji                         Food               10.000000
          SneaKare               Beauty/Fashion                7.000000
             Hoovu                     Services               50.000000
    VeryMuchIndian               Beauty/Fashion               25.000000
             Stage                Entertainment               50.000000
    GearHeadMotors Vehicles/Electrical Vehicles               50.000000
            TeaFit             Liquor/Beverages               12.500000
        Haqdarshak                     Services               33.330000
    TheSimplySalad                         Food               15.000000
 AtypicalAdvantage          Technology/Software               15.000000
 HouseOfChikankari               Beauty/Fashion               37.500000
          Paradyes               Beauty/Fashion               32.500000
         Primebook          Technology/Software               37.500000
         GharSoaps               Beauty/Fashion               60.000000
         InsideFPV                Manufacturing               18.750000
        FastBeetle                     Services               45.000000
         Bullspree          Technology/Software               37.500000
            Snitch               Beauty/Fashion               30.000000
             Portl                     Services               50.000000
            Dabble                Manufacturing               15.000000
          Broomees                     Services               33.330000
            Geeani Vehicles/Electrical Vehicles               33.330000
          Manetain               Beauty/Fashion               75.000000
            UnStop          Technology/Software               50.000000
           BlueTea                         Food               50.000000
              Zoff                         Food              100.000000
          Mahantam                Manufacturing                6.000000
         MindPeers               Medical/Health               17.660000
         Daryaganj                         Food               90.000000
       DhruvVidyut Vehicles/Electrical Vehicles                0.000000
   TheHealthyBinge                         Food               25.000000
         MeduLance               Medical/Health               66.660000
          neuphony               Medical/Health               50.000000
            Malaki             Liquor/Beverages               25.000000
           nawgati                     Services               33.500000
           GladFul                         Food               16.660000
       Pharmallama               Medical/Health               40.000000
              Hood          Technology/Software               30.000000
           Trunome               Medical/Health               37.500000
             Wol3D                Manufacturing               80.000000
  What'sUpWellness                         Food               20.000000
  ThePlatedProject                     Services               25.000000
            Rubans               Beauty/Fashion               33.330000
         Sahayatha               Medical/Health               20.000000
               TAC               Beauty/Fashion               40.500000
---------------------------------------------------------------------------

Aman industry wise investments

Industry
Food                            18
Beauty/Fashion                  14
Medical/Health                   8
Manufacturing                    7
Technology/Software              7
Services                         7
Vehicles/Electrical Vehicles     4
Education                        3
Liquor/Beverages                 2
Electronics                      1
Animal/Pets                      1
Entertainment                    1
Name: count, dtype: int64

🎾 Peyush Bansal's Investments¶

In [104]:
print("Total investments by Peyush", shark_tank[shark_tank['Peyush Investment Amount']>=0][['Peyush Investment Amount']].count().to_string()[-2:])
print("Investment amount by Peyush", round(shark_tank['Peyush Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Peyush", round(shark_tank['Peyush Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Peyush", round(shark_tank['Peyush Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Peyush Investment Amount']>=0][["Startup Name","Industry","Peyush Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nPeyush industry wise investments\n")
print(shark_tank[shark_tank['Peyush Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Peyush Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Peyush Investment Amount']>=0] [["Startup Name","Peyush Investment Amount","Peyush Investment Equity"]].sort_values(by="Peyush Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Peyush Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Peyush 69
Investment amount by Peyush 22.1 crores
Equity received by Peyush 422.11 % in different companies
Debt/loan amount by Peyush 3.42 crores

Company details:
---------------------------------------------------------------------------
      Startup Name                     Industry  Peyush Investment Amount
VivalyfInnovations               Medical/Health                 28.000000
             Ariro                Manufacturing                 25.000000
           Nuutjob               Beauty/Fashion                  8.330000
          Meatyour                         Food                 10.000000
         EventBeep                    Education                 10.000000
              LOKA          Technology/Software                 13.330000
             Annie                    Education                 35.000000
        Carragreen                Manufacturing                 25.000000
     TheYarnBazaar                Manufacturing                 25.000000
               PNT          Technology/Software                 25.000000
FindYourKicksIndia               Beauty/Fashion                 10.000000
      AasVidyalaya                    Education                 50.000000
        RoadBounce          Technology/Software                 80.000000
           WeSTOCK                  Animal/Pets                 15.000000
     TheStatePlate                         Food                 40.000000
            INACAN                         Food                 20.000000
      Sid07Designs                     Hardware                 25.000000
     HairOriginals               Beauty/Fashion                 20.000000
        KGAgrotech                  Agriculture                 10.000000
SunfoxTechnologies               Medical/Health                 20.000000
    IsakFragrances               Beauty/Fashion                 50.000000
 WattTechnovations               Medical/Health                  0.000253
 InsuranceSamadhan                     Services                100.000000
           HumpyA2                         Food                 33.330000
 GoldSafeSolutions                Manufacturing                 16.660000
         TweekLabs                       Sports                 20.000000
            Proxgy          Technology/Software                 50.000000
      StoreMyGoods                     Services                 25.000000
           WitBlox                Manufacturing                 30.000000
             Hoovu                     Services                 50.000000
             Dorji                         Food                 10.000000
             Stage                Entertainment                 50.000000
    GearHeadMotors Vehicles/Electrical Vehicles                 50.000000
         PatilKaki                         Food                 20.000000
            TeaFit             Liquor/Beverages                 12.500000
        Haqdarshak                     Services                 33.330000
 HouseOfChikankari               Beauty/Fashion                 37.500000
 ABCSports&Fitness                       Sports                 40.000000
         Primebook          Technology/Software                 37.500000
         InsideFPV                Manufacturing                 18.750000
             Kyari                Manufacturing                 25.500000
        FastBeetle                     Services                 45.000000
             Sepal                Manufacturing                 50.000000
           Solinas                     Services                 45.000000
            ekatra         Furnishing/Household                 10.000000
         NeoMotion                Manufacturing                100.000000
         Bullspree          Technology/Software                 37.500000
            Snitch               Beauty/Fashion                 30.000000
             Portl                     Services                 50.000000
          Broomees                     Services                 33.330000
           PadCare                Manufacturing                 25.000000
               OLL          Technology/Software                 15.000000
          Mahantam                Manufacturing                  6.000000
         MindPeers               Medical/Health                 53.000000
       DhruvVidyut Vehicles/Electrical Vehicles                  0.000000
             iMumz               Medical/Health                 10.000000
   TheHealthyBinge                         Food                 25.000000
           Perfora         Furnishing/Household                 26.660000
           CureSee               Medical/Health                 50.000000
         MeduLance               Medical/Health                 66.660000
          neuphony               Medical/Health                 50.000000
            Malaki             Liquor/Beverages                 25.000000
       Pharmallama               Medical/Health                 40.000000
              Hood          Technology/Software                 30.000000
            GROWiT                  Agriculture                 25.000000
           Trunome               Medical/Health                 37.500000
       SinghStyled               Beauty/Fashion                 50.000000
       LilGoodness                         Food                 50.000000
         Sahayatha               Medical/Health                 20.000000
---------------------------------------------------------------------------

Peyush industry wise investments

Industry
Medical/Health                  11
Manufacturing                   11
Food                             8
Technology/Software              8
Services                         8
Beauty/Fashion                   7
Education                        3
Agriculture                      2
Sports                           2
Vehicles/Electrical Vehicles     2
Liquor/Beverages                 2
Furnishing/Household             2
Animal/Pets                      1
Hardware                         1
Entertainment                    1
Name: count, dtype: int64

✳️ Amit Jain's Investments¶

In [105]:
print("Total investments by Amit", shark_tank[shark_tank['Amit Investment Amount']>=0][['Amit Investment Amount']].count().to_string()[-2:])
print("Investment amount by Amit", round(shark_tank['Amit Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Amit", round(shark_tank['Amit Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Amit", round(shark_tank['Amit Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Amit Investment Amount']>0][["Startup Name","Industry","Amit Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nAmit industry wise investments\n")
print(shark_tank[shark_tank['Amit Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Amit Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Amit Investment Amount']>0] [["Startup Name","Amit Investment Amount","Amit Investment Equity"]].sort_values(by="Amit Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Amit Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by Amit 21
Investment amount by Amit 8.01 crores
Equity received by Amit 109.0 % in different companies
Debt/loan amount by Amit 1.45 crores

Company details:
---------------------------------------------------------------------------
 Startup Name                     Industry  Amit Investment Amount
    InsideFPV                Manufacturing                   18.75
    Angrakhaa               Beauty/Fashion                   40.00
    MoppFoods                         Food                   75.00
       Dobiee                         Food                   72.00
        Pflow               Medical/Health                   30.00
       ekatra         Furnishing/Household                   10.00
    licksters                         Food                   25.00
   ScrapUncle                     Services                   60.00
       UnStop          Technology/Software                   50.00
  Cakelicious                         Food                   25.00
     Hornback Vehicles/Electrical Vehicles                   50.00
      nawgati                     Services                   33.50
      GladFul                         Food                   16.66
  Pharmallama               Medical/Health                   40.00
      funngro          Technology/Software                   25.00
       Aadvik                         Food                   15.00
ForeverModest               Beauty/Fashion                    5.00
    Sahayatha               Medical/Health                   20.00
       maisha               Beauty/Fashion                   10.00
     NishHair               Beauty/Fashion                  100.00
     StyloBug               Beauty/Fashion                   80.00
---------------------------------------------------------------------------

Amit industry wise investments

Industry
Food                            6
Beauty/Fashion                  5
Medical/Health                  3
Services                        2
Technology/Software             2
Manufacturing                   1
Furnishing/Household            1
Vehicles/Electrical Vehicles    1
Name: count, dtype: int64

🎆 All Guest's Investments¶

In [56]:
print("Total investments by all Guests", shark_tank[shark_tank['Guest Investment Amount']>=0][['Guest Investment Amount']].count().to_string()[-2:])
print("Investment amount by all Guests", round(shark_tank['Guest Investment Amount'].sum()/100, 2), "crores")
print("Equity received by all Guests", round(shark_tank['Guest Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by all Guests", round(shark_tank['Guest Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Guest Investment Amount']>0][["Startup Name","Industry","Guest Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nAll Guests industry wise investments\n")
print(shark_tank[shark_tank['Guest Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Guest Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Guest Investment Amount']>0] [["Startup Name","Guest Investment Amount","Guest Investment Equity"]].sort_values(by="Guest Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Guest Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()
Total investments by all Guests 37
Investment amount by all Guests 14.22 crores
Equity received by all Guests 123.7 % in different companies
Debt/loan amount by all Guests 1.95 crores

Company details:
---------------------------------------------------------------------------
        Startup Name                     Industry  Guest Investment Amount
          TheSassBar               Beauty/Fashion                25.000000
  SunfoxTechnologies               Medical/Health                20.000000
   WattTechnovations               Medical/Health                 0.000253
             HumpyA2                         Food                33.330000
   GoldSafeSolutions                Manufacturing                16.660000
          WakaoFoods                         Food                25.000000
    NomadFoodProject                         Food                10.000000
             WitBlox                Manufacturing                30.000000
                 TAC               Beauty/Fashion                40.500000
          Naara-Aaba             Liquor/Beverages                25.000000
              RodBez Vehicles/Electrical Vehicles                10.000000
                Blix          Technology/Software                40.000000
               TURMS               Beauty/Fashion               120.000000
             mintree               Beauty/Fashion                45.000000
            DilFoods                         Food               100.000000
         GoenchiFeni             Liquor/Beverages               200.000000
              GudGum                         Food                20.000000
            EvaScalp               Medical/Health                10.000000
          HoneyTwigs                         Food                25.000000
            JewelBox               Beauty/Fashion                80.000000
            DaakRoom                     Services                36.000000
         NasherMiles               Beauty/Fashion                60.000000
             Without               Beauty/Fashion                37.500000
                Kibo          Technology/Software                30.000000
            YesMadam               Beauty/Fashion                37.500000
ToffeeCoffeeRoasters             Liquor/Beverages                35.000000
            Chefling                         Food                10.000000
             ToHands          Technology/Software                60.000000
            PlusGold          Technology/Software                60.000000
             Aroleap               Medical/Health                25.000000
            WiseLife               Medical/Health                30.000000
          ModelVerse          Technology/Software                 8.330000
              MEPACK                     Services                 3.500000
               Deeva               Beauty/Fashion                50.000000
                Sama                     Services                33.330000
           Dharaksha                Manufacturing                 0.002500
        iDreamCareer          Technology/Software                30.000000
---------------------------------------------------------------------------

All Guests industry wise investments

Industry
Beauty/Fashion                  9
Food                            7
Technology/Software             6
Medical/Health                  5
Manufacturing                   3
Liquor/Beverages                3
Services                        3
Vehicles/Electrical Vehicles    1
Name: count, dtype: int64
In [57]:
# Guest sharks and number of companies they invested
shark_tank.loc[shark_tank['Guest Investment Amount'] > 1]['Invested Guest Name'].str.split(',').explode('Guest Name').value_counts().sort_values(ascending=False)
Out[57]:
Invested Guest Name
Ritesh Aggarwal     17
Ghazal Alagh         7
Azhar Iqubal         4
Radhika Gupta        4
Vikas D Nahar        2
Ronnie Screwvala     2
Varun Dua            2
Deepinder Goyal      1
Name: count, dtype: int64
In [58]:
# Investment amount by guests, in lakhs
round(shark_tank.groupby(["Invested Guest Name"])["Guest Investment Amount"].sum().sort_values(ascending=False))
Out[58]:
Invested Guest Name
Ritesh Aggarwal                  379.0
Ritesh Aggarwal,Radhika Gupta    230.0
Azhar Iqubal                     200.0
Deepinder Goyal                  200.0
Ghazal Alagh                     160.0
Ronnie Screwvala                  68.0
Vikas D Nahar                     66.0
Varun Dua                         60.0
Varun Dua,Radhika Gupta           60.0
Name: Guest Investment Amount, dtype: float64
In [59]:
# tmpdf = shark_tank.loc[shark_tank['Guest Investment Amount'] > 1]
# tmpdf[['Invested Guest Name','Guest Investment Amount']]
In [60]:
# Number of sharks in a deal, in all seasons
print(shark_tank['Number of Sharks in Deal'].value_counts(), "\n")

# In percentage
print(round(shark_tank['Number of Sharks in Deal'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%', regex=False))

fig = plt.figure(figsize=(8, 6))
plt.title("Number of sharks in a deal, in all seasons", fontsize=15)
plt.xticks(fontsize=15)
plt.yticks([])
ax = sns.countplot(data = shark_tank, x = 'Number of Sharks in Deal')
ax.set_ylabel('')
for t in ax.patches:
    if (np.isnan(float(t.get_height()))):
        ax.annotate(0, (t.get_x(), 0))
    else:
        ax.annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.01), size=14)
Number of Sharks in Deal
1.0    107
2.0     75
3.0     36
4.0     18
5.0     12
Name: count, dtype: int64 

Number of Sharks in Deal
1.0    43%
2.0    30%
3.0    15%
4.0     7%
5.0     5%
Name: proportion, dtype: object
In [61]:
# All sharks deals
print(shark_tank.loc[shark_tank['Number of Sharks in Deal'] >= 5][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity"]])
     Season Number        Startup Name  Total Deal Amount  Total Deal Equity
15               1       SkippiIcePops           100.0000               15.0
50               1  FindYourKicksIndia            50.0000               25.0
64               1              INACAN           100.0000               10.0
80               1  SunfoxTechnologies           100.0000                6.0
209              2              Snitch           150.0000                1.5
239              2            Mahantam            30.0000               20.0
274              2         Pharmallama           200.0000                5.0
311              2           Sahayatha           100.0000               10.0
357              3            JewelBox           200.0000                6.0
365              3         NasherMiles           300.0000                1.5
423              3           LittleBox            75.0000                2.5
435              3           Dharaksha             0.0125                1.0
In [62]:
# Sharks with most number of solo deals
amt_cols = shark_tank.columns[shark_tank.columns.str.contains(' Investment Amount')].tolist()
tmp = shark_tank.loc[shark_tank['Number of Sharks in Deal'] == 1][amt_cols]
tmp.count().sort_values(ascending=False).nlargest(3)

# Namita did most number of solo deals, than any other Shark
Out[62]:
Namita Investment Amount    24
Aman Investment Amount      21
Peyush Investment Amount    17
dtype: int64
In [63]:
# Sharks with most number of episode presence, in all seasons
present_cols = shark_tank.columns[shark_tank.columns.str.endswith(' Present')].tolist()
tmp = shark_tank[present_cols]
tmp.sum().sort_values(ascending=False).nlargest(3)

# Anupam was there in most number of episodes, in 3 seasons
Out[63]:
Anupam Present    390.0
Aman Present      383.0
Namita Present    361.0
dtype: float64
In [64]:
# Sharks with most number of episode presence, in current/latest season (3rd Season)
tmp = shark_tank.loc[shark_tank['Season Number'] == 3][present_cols]
tmp.sum().sort_values(ascending=False).nlargest(4)
Out[64]:
Aman Present       111.0
Anupam Present     108.0
Guest Present      102.0
Vineeta Present     96.0
dtype: float64
In [65]:
# Anchor and number of pitches, they hosted
shark_tank.groupby('Anchor').size()
Out[65]:
Anchor
Rahul Dua          289
Rannvijay Singh    152
dtype: int64
In [66]:
# Anchor and number of episodes, they hosted
pd.pivot_table(shark_tank, values='Episode Number', columns='Anchor', aggfunc='max')
Out[66]:
Anchor Rahul Dua Rannvijay Singh
Episode Number 51 36
In [67]:
# Sharks
# tmp = shark_tank.loc[shark_tank['Number of Sharks in Deal'] == 2][amt_cols].stack()
# tmp
# tmp2 = shark_tank.loc[shark_tank['Number of Sharks in Deal'] == 2][amt_cols].transpose()
# tmp2
In [68]:
print(shark_tank['Pitchers State'].str.split(',').explode('Pitchers State').value_counts(), "\n")
shark_tank['Pitchers State'].str.split(',').explode('Pitchers State').value_counts().sort_values().plot.barh()
Pitchers State
Maharashtra          127
Delhi                 67
Karnataka             52
Gujarat               46
Haryana               30
Uttar Pradesh         24
West Bengal           19
Rajasthan             17
Telangana             16
Punjab                12
Tamil Nadu             9
Madhya Pradesh         8
Bihar                  5
Goa                    4
Jammu & Kashmir        4
Kerala                 4
Uttarakhand            3
Jharkhand              3
Himachal Pradesh       2
Chhattisgarh           2
Assam                  2
Andhra Pradesh         1
Arunachal Pradesh      1
Tamilnadu              1
 Bihar                 1
 Kerala                1
Name: count, dtype: int64 

Out[68]:
<Axes: ylabel='Pitchers State'>
In [107]:
# Top 20 Indian Cities
tmp = shark_tank['Pitchers City'].value_counts().nlargest(20).sort_values(ascending=True)
fig = px.bar(tmp, x=tmp.values, title="<b>Indian top 20 cities</b> with number of startups came for pitching", template='simple_white', text=tmp, width=850, height=800)
fig.update_yaxes(title_text="")
fig.update_xaxes(visible=False)
fig.show()
In [70]:
# Most frequently asked amount, by startups
shark_tank.groupby('Original Ask Amount').size().nlargest(10)

# Original Ask Amount (in lakhs) and Number of times asked
Out[70]:
Original Ask Amount
50.0     94
100.0    77
75.0     52
60.0     27
40.0     22
80.0     22
150.0    20
30.0     19
90.0     11
200.0    11
dtype: int64
In [71]:
# Most frequently offered equity, by startups
shark_tank.groupby('Original Offered Equity').size().nlargest(10)

# Original Offered Equity (in %) and Number of times offered
Out[71]:
Original Offered Equity
1.0     89
2.0     67
5.0     67
10.0    40
3.0     37
4.0     26
2.5     25
0.5     17
1.5     15
7.5     11
dtype: int64
In [72]:
# ✅ Most frequently invested amount, by Sharks
shark_tank.groupby('Total Deal Amount').size().nlargest(10)

# Sharks mostly invested between 50K-1lakh per deal

# Total Deal Amount (in lakhs) and Number of times invested
Out[72]:
Total Deal Amount
50.0     47
100.0    39
75.0     24
60.0     20
40.0     14
30.0     13
25.0     12
20.0      9
80.0      9
10.0      7
dtype: int64
In [73]:
# shark_tank.groupby(['Original Ask Amount','Received Offer']).size().nlargest(100)
In [74]:
# ✅ Most frequently received total equity, by Sharks
shark_tank.groupby('Total Deal Equity').size().nlargest(10)

# Sharks are expecting around 10-20% equity, in a deal

# Total Deal Equity (in %) and Number of times invested
Out[74]:
Total Deal Equity
10.0    30
1.0     23
5.0     23
2.0     17
4.0     17
3.0     16
20.0    16
15.0    14
6.0     13
2.5      9
dtype: int64
In [75]:
# ✅ Mostly successful combinations (of asked amount and offered equity)
shark_tank.loc[shark_tank['Received Offer'] == 1].groupby(['Original Ask Amount','Original Offered Equity']).size().nlargest(10)
Out[75]:
Original Ask Amount  Original Offered Equity
50.0                 5.0                        16
100.0                1.0                        15
50.0                 2.0                        10
100.0                2.0                        10
60.0                 2.0                         8
50.0                 1.0                         7
                     3.0                         7
75.0                 4.0                         7
50.0                 10.0                        6
70.0                 1.0                         6
dtype: int64
In [76]:
# Most frequently asked amount, by startups who could NOT get a deal
shark_tank.loc[shark_tank['Received Offer'] == 0].groupby('Original Ask Amount').size().nlargest(10)

# Original Ask Amount (in lakhs) and Number of times asked (but rejected by sharks) ❌
Out[76]:
Original Ask Amount
50.0     30
100.0    23
75.0     16
60.0      9
80.0      8
40.0      6
90.0      6
200.0     6
20.0      4
25.0      4
dtype: int64
In [77]:
# Most frequently offered equity, by startups who could NOT get a deal
shark_tank.loc[shark_tank['Received Offer'] == 0].groupby('Original Offered Equity').size().nlargest(10)

# Original Offered Equity (in %) and Number of times offered (but rejected by sharks) ❌
Out[77]:
Original Offered Equity
5.0     29
1.0     24
2.0     17
10.0    14
3.0     12
2.5     11
1.5      4
4.0      4
7.5      4
15.0     4
dtype: int64
In [78]:
# Mostly rejected combinations (of asked amount and offered equity)
shark_tank.loc[shark_tank['Received Offer'] == 0].groupby(['Original Ask Amount','Original Offered Equity']).size().nlargest(5)

# You may not get deal, if you ask for 1 crore with 1% equity or 50K with 5%/10% equity 🔴
Out[78]:
Original Ask Amount  Original Offered Equity
100.0                1.0                        9
50.0                 5.0                        8
                     10.0                       5
75.0                 5.0                        5
60.0                 2.0                        4
dtype: int64
In [79]:
shp_gdf = gpd.read_file('../input/india-gis-data/India States/Indian_states.shp')
merged = shp_gdf.set_index('st_nm').join(shark_tank.set_index('Pitchers State'))
merged['Total Deal Amount'] = merged['Total Deal Amount'].fillna(0)

fig, ax = plt.subplots(1, figsize=(12, 12))
ax.axis('off')
ax.set_title('As per Total Deal Amount', fontdict={'fontsize': '15', 'fontweight' : '3'})
fig = merged.plot(column='Total Deal Amount', cmap='YlGn', linewidth=0.8, ax=ax, edgecolor='0.5', legend=True)
In [80]:
# All season's, startup companies incorporated year
fig = plt.figure(figsize=(10, 7))
plt.title('Number of companies and Year of establishment of startups', size=14)
tmp = shark_tank.loc[shark_tank['Started in'].notnull()]
ax = sns.countplot(data = tmp, x = 'Started in')
ax.set_xlabel('Started in', fontsize=13)
plt.yticks([])
ax.set_ylabel('')
for t in ax.patches:
    if (np.isnan(float(t.get_height()))):
        ax.annotate(0, (t.get_x(), 0))
    else:
        ax.annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.02), size=14)
In [81]:
# Some companies got more amount than they asked/expected
print(shark_tank.loc[shark_tank['Original Ask Amount'] < shark_tank["Total Deal Amount"]][["Startup Name"]].count())
shark_tank.loc[shark_tank['Original Ask Amount'] < shark_tank["Total Deal Amount"]][["Season Number","Startup Name","Original Ask Amount","Total Deal Amount"]]
Startup Name    35
dtype: int64
Out[81]:
Season Number Startup Name Original Ask Amount Total Deal Amount
0 1 BluePineFoods 50.0 75.0
10 1 JhaJiAchaar 50.0 56.6
15 1 SkippiIcePops 45.0 100.0
37 1 Annie 30.0 105.0
39 1 TheYarnBazaar 50.0 100.0
43 1 HammerLifestyle 30.0 100.0
59 1 WeSTOCK 50.0 60.0
64 1 INACAN 50.0 100.0
76 1 TheSassBar 40.0 50.0
89 1 HumpyA2 75.0 100.0
109 1 TweekLabs 40.0 60.0
110 1 Proxgy 35.0 100.0
118 1 SneaKare 20.0 21.0
152 2 Hoovu 80.0 100.0
161 2 GearHeadMotors 75.0 100.0
178 2 Zillionaire 50.0 100.0
216 2 Broomees 80.0 100.0
219 2 PadCare 50.0 100.0
223 2 Geeani 75.0 100.0
230 2 UnStop 100.0 200.0
240 2 MindPeers 53.0 106.0
257 2 CureSee 40.0 50.0
274 2 Pharmallama 100.0 200.0
283 2 What'sUpWellness 50.0 60.0
333 3 DilFoods 50.0 200.0
334 3 AIKavach/Panoplia 50.0 100.0
337 3 Kalakaram 50.0 60.0
343 3 WYLDCard 50.0 75.0
348 3 GoenchiFeni 100.0 200.0
351 3 GudGum 50.0 80.0
357 3 JewelBox 100.0 200.0
363 3 ALittleExtra 48.0 60.0
372 3 HyperLab 10.0 25.0
394 3 ToHands 55.0 60.0
405 3 WiseLife 60.0 120.0
In [82]:
# Most of the companies diluted/gave their company equity more than they initially offered/expected
shark_tank.loc[shark_tank['Original Offered Equity'] < shark_tank["Total Deal Equity"]][["Season Number","Startup Name","Original Offered Equity","Total Deal Equity"]]
Out[82]:
Season Number Startup Name Original Offered Equity Total Deal Equity
0 1 BluePineFoods 5.0 16.00
1 1 BoozScooters 15.0 50.00
2 1 HeartUpMySleeves 10.0 30.00
3 1 TagzFoods 1.0 2.75
7 1 Peeschute 4.0 6.00
... ... ... ... ...
424 3 CremeCastle 1.5 2.50
427 3 Deeva 4.0 6.00
428 3 DesignTemplate 2.5 10.00
429 3 Sama 1.0 1.50
440 3 Smotect 1.0 5.00

199 rows × 4 columns

In [83]:
# Below (19) companies got the same valuation they requested (with or without loan)
print(shark_tank.loc[shark_tank['Valuation Requested'] == shark_tank["Deal Valuation"]][["Startup Name"]].count())
shark_tank.loc[shark_tank['Valuation Requested'] == shark_tank["Deal Valuation"]][["Season Number","Startup Name","Valuation Requested","Deal Valuation"]]
Startup Name    28
dtype: int64
Out[83]:
Season Number Startup Name Valuation Requested Deal Valuation
20 1 Kavach 50.00 50.00
22 1 BeyondSnack 2000.00 2000.00
45 1 Cocofit 0.00 0.00
86 1 WattTechnovations 0.00 0.00
171 2 TheSimplySalad 300.00 300.00
185 2 Janitri 4000.00 4000.00
203 2 NeoMotion 10000.00 10000.00
219 2 PadCare 2500.00 2500.00
223 2 Geeani 1000.00 1000.00
240 2 MindPeers 5300.00 5300.00
243 2 DhruvVidyut 0.00 0.00
253 2 TheHealthyBinge 1000.00 1000.00
311 2 Sahayatha 1000.00 1000.00
314 2 NishHair 5000.00 5000.00
334 3 AIKavach/Panoplia 4000.00 4000.00
341 3 WeHear 25000.00 25000.00
354 3 HoneyTwigs 2500.00 2500.00
355 3 Koparo 7000.00 7000.00
363 3 ALittleExtra 800.00 800.00
390 3 Matri 1500.00 1500.00
397 3 Cosmix 10000.00 10000.00
399 3 PolishMePretty 100.00 100.00
405 3 WiseLife 3000.00 3000.00
408 3 AvataarSkincare 7000.00 7000.00
412 3 ModelVerse 250.00 250.00
414 3 TheShellHair 1000.00 1000.00
416 3 MEPACK 70.00 70.00
435 3 Dharaksha 1.25 1.25
In [84]:
# There is 1 company which got more valuation than they pitched, JhaJi Achaar received after the Season (in 2023)
shark_tank.loc[shark_tank['Valuation Requested'] < shark_tank["Deal Valuation"]][["Startup Name","Valuation Requested","Deal Valuation"]]
Out[84]:
Startup Name Valuation Requested Deal Valuation
10 JhaJiAchaar 500.0 1007.0
372 HyperLab 1000.0 2500.0
In [85]:
# Some companies were on pre-revenue or didn't had any revenue (as of pitching day)
shark_tank.loc[shark_tank['Yearly Revenue'] == 0]
Out[85]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present
9 1 CosIQ 4 10 20-Dec-21 4-Feb-22 23-Dec-21 Entrepreneurship Ki Wave Rannvijay Singh Beauty/Fashion Intelligent Skincare https://mycosiq.com/ 2021 2 1 1 <NA> 1 Middle Delhi Delhi 0 4.0 75 20 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0 NaN
23 1 VivalyfInnovations 8 24 20-Dec-21 4-Feb-22 29-Dec-21 Shark Ko Impress Karne Wale Ideas Rannvijay Singh Medical/Health Easy Life Prickless Diabetes Testing Machine https://vivalyf.in/ 2021 2 1 1 <NA> 0 Young Hyderabad Telangana 0 NaN <NA> <NA> ... NaN NaN NaN 28.0 16.66 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 NaN 1.0 NaN
24 1 MotionBreeze 8 25 20-Dec-21 4-Feb-22 29-Dec-21 Shark Ko Impress Karne Wale Ideas Rannvijay Singh Vehicles/Electrical Vehicles Smart Electric Motorcycle https://www.motionautomotive.in/ 2018 4 4 <NA> <NA> 0 Middle Vadodara Gujarat 0 NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 30.0 6.0 NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 NaN 1.0 NaN
42 1 GoodGoodPiggy 14 43 20-Dec-21 4-Feb-22 6-Jan-22 Naye Aur Nayab Pitchers Rannvijay Singh Technology/Software Digital Piggy Bank https://goodgoodpiggy.com/ 2021 2 <NA> 2 <NA> 0 Young Delhi Delhi 0 NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 NaN 1.0 NaN
77 1 KGAgrotech 24 78 20-Dec-21 4-Feb-22 20-Jan-22 A Decade Of Indian Entrepreneurship Rannvijay Singh Agriculture Agricultural Innovations https://www.instagram.com/jugaadu_kamlesh/ 2022 2 2 <NA> <NA> 0 Young Malegaon Maharashtra 0 NaN <NA> <NA> ... NaN NaN NaN 10.0 40.00 20.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Ghazal Alagh 1.0 1.0 1.0 NaN 1.0 NaN NaN 1.0
83 1 JulaaAutomation 26 84 20-Dec-21 4-Feb-22 24-Jan-22 Revolutionary Ideas Rannvijay Singh Manufacturing Automatic Cradle https://www.automaticjulaa.com/ 2022 3 3 <NA> <NA> 0 Middle Ahmedabad Gujarat 0 NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Ghazal Alagh 1.0 1.0 1.0 NaN 1.0 NaN NaN 1.0
98 1 Scholify 30 99 20-Dec-21 4-Feb-22 28-Jan-22 Sharks Ki Expertise Rannvijay Singh Education Scholarship Platform https://scholifyme.com/ 2018 1 1 <NA> <NA> 0 Middle Bangalore Karnataka 0 NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0 NaN
100 1 Sabjikothi 31 101 20-Dec-21 4-Feb-22 31-Jan-22 Entrepreneurship Ki Raah Rannvijay Singh Manufacturing Vegetables Storage SaptKrishi https://www.saptkrishi.com/ 2019 2 1 1 <NA> 0 Young Bhagalpur Bihar 0 NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0 NaN
114 1 On2Cook 34 115 20-Dec-21 4-Feb-22 3-Feb-22 Scaling Ambitions Rannvijay Singh Food Fastest Cooking Device https://on2cook.com/ 2022 1 1 <NA> <NA> 0 Middle Ahmedabad Gujarat 0 NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Ghazal Alagh 1.0 1.0 1.0 1.0 1.0 NaN 1.0 1.0
131 1 Scintiglo 0 132 20-Dec-21 4-Feb-22 NaN Unseen Rannvijay Singh Medical/Health Diagnostic device for microalbuminuria estimation https://cemd.in/ 2021 1 1 <NA> <NA> 0 Middle Indore Madhya Pradesh 0 NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
205 2 Sayonara 18 206 2-Jan-23 10-Mar-23 25-Jan-23 Business Ideas With Potential Rahul Dua Beauty/Fashion Petticoat NaN <NA> 1 1 <NA> <NA> 0 Middle Kolkata West Bengal 0 NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 1.0 NaN NaN
206 2 PMV 19 207 2-Jan-23 10-Mar-23 26-Jan-23 Building Brands For India Rahul Dua Vehicles/Electrical Vehicles Personal Mobility Vehicle https://pmvelectric.com/ 2018 1 1 <NA> <NA> 0 Middle Mumbai Maharashtra 0 NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
257 2 CureSee 34 258 2-Jan-23 10-Mar-23 16-Feb-23 Growing Ideas Into Successful Businesses Rahul Dua Medical/Health Artificial Intelligence (AI) based vision therapy https://curesee.com/ 2019 3 3 <NA> <NA> 0 Middle Gurgaon Haryana 0 264.0 <NA> <NA> ... NaN NaN NaN 50.0 10.00 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
264 2 HoloKitab 36 265 2-Jan-23 10-Mar-23 20-Feb-23 Anokhe Pitchers Ke Anokhe Ideas Rahul Dua Technology/Software Augmented Reality content for books https://www.holokitab.in/ <NA> 2 2 <NA> <NA> 0 Middle Jalandhar Punjab 0 NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
277 2 Hood 40 278 2-Jan-23 10-Mar-23 24-Feb-23 Creating Valuable Businesses Rahul Dua Technology/Software Pseudonymous social network https://www.hood.live/ 2022 3 3 <NA> <NA> 0 Middle Gurgaon Haryana 0 NaN <NA> <NA> ... 30.0 0.27 30.0 30.0 0.27 30.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 1.0 NaN NaN
295 2 WaggyZone 44 296 2-Jan-23 10-Mar-23 2-Mar-23 Entrepreneurship Ka Junoon Rahul Dua Animal/Pets Ice Cream Treat for Dogs, Puppies and Cats https://waggyzone.com/ <NA> 1 <NA> 1 <NA> 0 Middle Mumbai Maharashtra 0 1.0 60 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
350 3 Vecros 10 351 22-Jan-24 NaN 2-Feb-24 Pitching Innovation Rahul Dua Technology/Software Spatial AI drone https://vecros.com/ 2018 2 1 1 <NA> 0 Young Delhi Delhi 0 9.0 <NA> <NA> ... 20.0 1.00 80.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Deepinder Goyal 1.0 1.0 1.0 1.0 NaN NaN NaN 1.0
415 3 Rize 32 416 22-Jan-24 NaN 5-Mar-24 Young Entrepreneurs Make Their Mark Rahul Dua Food Energy Bars https://rizebar.in/ 2023 2 2 <NA> <NA> 0 Young Gurgaon Haryana 0 NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Ritesh Aggarwal 1.0 NaN 1.0 1.0 NaN 1.0 NaN 1.0

18 rows × 78 columns

In [86]:
# Some companies were on burning/paying money from their pocket, without any profit (as of pitching day)
shark_tank.loc[shark_tank['Cash Burn'] == 'yes']
Out[86]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present
62 1 TheStatePlate 20 63 20-Dec-21 4-Feb-22 14-Jan-22 A Variety Of Ideas Rannvijay Singh Food Delicacies https://thestateplate.com/ 2020 2 1 1 <NA> 0 Young Bangalore,Kolkata Karnataka,West Bengal <NA> 40.0 34 <NA> ... NaN NaN NaN 40.00 3.000 25.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 NaN 1.0 NaN
81 1 Alpino 25 82 20-Dec-21 4-Feb-22 21-Jan-22 An Ocean Of Opportunities Rannvijay Singh Food Roasted Peanut butter Products https://alpino.store/ 2016 4 4 <NA> <NA> 0 Young Surat Gujarat <NA> NaN 38 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Ghazal Alagh 1.0 1.0 1.0 NaN 1.0 NaN NaN 1.0
105 1 GrowFitter 32 106 20-Dec-21 4-Feb-22 1-Feb-22 The Road To Success Rannvijay Singh Technology/Software Rewards App https://www.growfitter.com/ 2021 2 2 <NA> <NA> 0 Middle Mumbai Maharashtra 170 NaN <NA> <NA> ... 50.00 2.0000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 NaN 1.0 NaN
137 1 ZyppElectric 0 138 20-Dec-21 4-Feb-22 NaN Unseen Rannvijay Singh Vehicles/Electrical Vehicles Electrical Vehicles https://zypp.app/ 2017 2 1 1 <NA> 1 Young Gurgaon Haryana <NA> NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
140 1 HappyBar 0 141 20-Dec-21 4-Feb-22 NaN Unseen Rannvijay Singh Food FitSport delicious snacks https://www.fitsport.me/ 2019 3 2 1 <NA> 0 Middle Hyderabad Telangana <NA> 29.0 <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
157 2 SoupX 2 158 2-Jan-23 10-Mar-23 3-Jan-23 A Bigger Vision Rahul Dua Food Soup based meals https://www.soupx.in/ <NA> 2 2 <NA> <NA> 0 Young Delhi Delhi <NA> NaN 45 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
165 2 Flatheads 5 166 2-Jan-23 10-Mar-23 6-Jan-23 Investing in the Future of India Rahul Dua Beauty/Fashion Shoes Sneakers Loafers https://www.flatheads.in/ 2019 1 1 <NA> <NA> 0 Middle Bangalore Karnataka <NA> NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
194 2 FastBeetle 15 195 2-Jan-23 10-Mar-23 20-Jan-23 Changing The Face Of Indian Entrepreneurship Rahul Dua Services Local courier and parcel service https://www.fastbeetle.com/ 2019 2 2 <NA> <NA> 0 Young Srinagar Jammu & Kashmir <NA> 25.0 54 <NA> ... 45.00 3.7500 NaN 45.00 3.750 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 1.0 NaN NaN
196 2 VSMani 16 197 2-Jan-23 10-Mar-23 23-Jan-23 Pitchers Ki Taiyyari Rahul Dua Food Coffee and snacks https://vsmani.com/ 2020 3 3 <NA> <NA> 0 Middle Bangalore Karnataka <NA> 63.0 <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 1.0 NaN NaN
208 2 Bullspree 19 209 2-Jan-23 10-Mar-23 26-Jan-23 Building Brands For India Rahul Dua Technology/Software App to learn stock market basics https://bullspree.com/ <NA> 3 3 <NA> <NA> 0 Middle Ahmedabad Gujarat <NA> NaN <NA> <NA> ... 37.50 1.4300 NaN 37.50 1.430 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
214 2 CloudTailor 21 215 2-Jan-23 10-Mar-23 30-Jan-23 Adhbhut Aur Anokhe Entrepreneurs Rahul Dua Services Custom tailor online https://www.cloudtailor.com/ <NA> 3 2 1 <NA> 1 Middle Bangalore,Hyderabad Karnataka,Telangana <NA> 34.0 <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
219 2 PadCare 23 220 2-Jan-23 10-Mar-23 1-Feb-23 Changing The World Rahul Dua Manufacturing Menstrual hygiene disposal solution https://www.padcarelabs.com/ <NA> 1 1 <NA> <NA> 0 Young Pune Maharashtra <NA> 14.0 <NA> <NA> ... NaN NaN NaN 25.00 1.000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
232 2 TheGreenSnack 27 233 2-Jan-23 10-Mar-23 7-Feb-23 Nayi Soch Naye Vichaar Rahul Dua Food Healthy Snacks Online https://thegreensnackco.com/ 2017 2 1 1 <NA> 1 Middle Mumbai Maharashtra <NA> 25.0 <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN 1.0 NaN NaN
241 2 Barosi 29 242 2-Jan-23 10-Mar-23 9-Feb-23 Pulse Of The Country Rahul Dua Food Fresh & pure milk products https://www.barosi.in/ 2016 1 1 <NA> <NA> 0 Middle Pataudi Haryana <NA> 42.0 40 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
247 2 Nirmalaya 31 248 2-Jan-23 10-Mar-23 13-Feb-23 Innovations And Investments Rahul Dua Manufacturing Incense products made from temple flowers https://nirmalaya.com/ <NA> 3 2 1 <NA> 1 Middle Delhi Delhi <NA> 80.0 <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
253 2 TheHealthyBinge 33 254 2-Jan-23 10-Mar-23 15-Feb-23 Growing With India Rahul Dua Food Assorted Pack Baked Chips https://www.healthybinge.co.in/ <NA> 2 2 <NA> <NA> 0 Middle Pune Maharashtra <NA> 11.0 <NA> <NA> ... 25.00 2.5000 NaN 25.00 2.500 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
254 2 Freakins 33 255 2-Jan-23 10-Mar-23 15-Feb-23 Growing With India Rahul Dua Beauty/Fashion Fashionable Denim Apparel https://freakins.com/ <NA> 2 2 <NA> <NA> 0 Middle Mumbai Maharashtra <NA> NaN 63 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
255 2 Perfora 34 256 2-Jan-23 10-Mar-23 16-Feb-23 Growing Ideas Into Successful Businesses Rahul Dua Furnishing/Household Toothpaste Electric Toothbrush https://perforacare.com/ <NA> 2 2 <NA> <NA> 0 Young Ballarpur,Karnal Haryana,Maharashtra <NA> NaN 57 <NA> ... NaN NaN NaN 26.66 0.833 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
262 2 neuphony 36 263 2-Jan-23 10-Mar-23 20-Feb-23 Anokhe Pitchers Ke Anokhe Ideas Rahul Dua Medical/Health Wearable EEG Headband https://neuphony.com/ 2022 2 1 1 <NA> 1 Young Noida Uttar Pradesh <NA> NaN <NA> <NA> ... 50.00 2.7000 NaN 50.00 2.700 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
273 2 GladFul 39 274 2-Jan-23 10-Mar-23 23-Feb-23 Revolutionary Ideas And Successful Businesses Rahul Dua Food Natural High Protein Rich & Healthy Foods https://gladful.in/ 2022 2 1 1 <NA> 0 Middle Jaipur Rajasthan <NA> 24.0 <NA> <NA> ... 16.66 1.1666 NaN NaN NaN NaN 16.66 1.1666 NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 1.0 NaN NaN
279 2 GROWiT 40 280 2-Jan-23 10-Mar-23 24-Feb-23 Creating Valuable Businesses Rahul Dua Agriculture Protective farming products https://thegrowit.com/ <NA> 2 2 <NA> <NA> 0 Middle Surat Gujarat <NA> NaN 22 <NA> ... NaN NaN NaN 25.00 0.500 25.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 1.0 NaN NaN
284 2 ProostBeer 42 285 2-Jan-23 10-Mar-23 28-Feb-23 Building Businesses From Scratch Rahul Dua Liquor/Beverages Freshly brewed beer https://www.proost69.com/ 2018 2 2 <NA> <NA> 0 Middle Delhi Delhi <NA> NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 1.0 NaN NaN
292 2 HealthyMaster 44 293 2-Jan-23 10-Mar-23 2-Mar-23 Entrepreneurship Ka Junoon Rahul Dua Food Online Dry Fruits, Snacks, Berries, Chips https://healthymaster.in/ 2019 3 1 2 <NA> 1 Middle Bangalore Karnataka <NA> 20.0 45 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
302 2 TheHealthyFactory 46 303 2-Jan-23 10-Mar-23 6-Mar-23 Different Colours Of Entrepreneurship Rahul Dua Food Protein bread https://www.thehealthfactory.in/ 2018 2 2 <NA> <NA> 0 Young Mumbai Maharashtra <NA> NaN 56 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
309 2 LilGoodness 48 310 2-Jan-23 10-Mar-23 8-Mar-23 Pitchers, Investments And Businesses Rahul Dua Food Healthy Snacks https://lilgoodness.com/ 2020 2 2 <NA> <NA> 0 Middle Bangalore Karnataka <NA> 145.0 65 <NA> ... NaN NaN NaN 50.00 1.000 50.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 1.0 NaN NaN
312 2 WickedGud 49 313 2-Jan-23 10-Mar-23 9-Mar-23 Businesses Adding Value To Society Rahul Dua Food High Protein & Fiber Gluten Free Vegan food https://wickedgud.com/ 2021 3 3 <NA> <NA> 0 Middle Mumbai Maharashtra <NA> NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 1.0 NaN NaN
315 2 MYBYK 50 316 2-Jan-23 10-Mar-23 10-Mar-23 Season Finale With The Sharks Rahul Dua Vehicles/Electrical Vehicles IoT-enabled bikes https://mybyk.in/ <NA> 1 1 <NA> <NA> 0 Middle Ahmedabad Gujarat <NA> NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 1.0 NaN NaN
316 2 GODESi 51 317 2-Jan-23 10-Mar-23 10-Mar-23 Gateway To Shark Tank India Rahul Dua Food Handmade lollipops https://godesi.in/ <NA> 2 1 1 <NA> 0 Middle Bangalore Karnataka <NA> 270.0 <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Vikas D Nahar 1.0 1.0 NaN 1.0 NaN 1.0 NaN 1.0
317 2 TAC 51 318 2-Jan-23 10-Mar-23 10-Mar-23 Gateway To Shark Tank India Rahul Dua Beauty/Fashion ayurveda co for glowing skin, makeup & open pores https://theayurvedaco.com/ <NA> 2 1 1 <NA> 1 Middle Mumbai Maharashtra <NA> NaN <NA> <NA> ... 40.50 0.5000 34.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN 40.5 0.50 34.5 Vikas D Nahar Vikas D Nahar 1.0 1.0 NaN 1.0 NaN 1.0 NaN 1.0
320 2 ZenOnco 0 321 2-Jan-23 10-Mar-23 NaN Unseen Rahul Dua Medical/Health saving lives from cancer https://zenonco.io/ <NA> 2 1 1 <NA> 0 Middle Jodhpur Rajasthan <NA> 21.0 <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Vikas D Nahar 1.0 1.0 NaN 1.0 NaN 1.0 NaN 1.0
322 3 AdilQadri 1 323 22-Jan-24 NaN 22-Jan-24 Bigger Better and Smarter Rahul Dua Beauty/Fashion Perfumes and Attar https://www.adilqadri.com/ 2020 1 1 <NA> <NA> 0 Young Bilimora Gujarat 2070 600.0 70 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN 1.0 NaN NaN
327 3 RodBez 3 328 22-Jan-24 NaN 24-Jan-24 Quest For Investment Rahul Dua Vehicles/Electrical Vehicles Taxi-service for Bihar https://rodbez.in/ 2022 2 2 <NA> <NA> 0 Middle Saharsa Bihar <NA> 6.0 <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 10.0 2.50 15.0 Ritesh Aggarwal Ritesh Aggarwal NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0
329 3 Homversity 3 330 22-Jan-24 NaN 24-Jan-24 Quest For Investment Rahul Dua Technology/Software Digital student housing app https://www.homversity.com/ 2022 1 1 <NA> <NA> 0 Young Ahmedabad Gujarat <NA> 100.0 <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Ritesh Aggarwal NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0
335 3 Bartisans 5 336 22-Jan-24 NaN 26-Jan-24 Innovative Ventures Vie For Sharks' Favour Rahul Dua Liquor/Beverages Cocktail Mocktail mixers https://www.bartisans.in/ 2021 2 1 1 <NA> 0 Middle Mumbai Maharashtra 143 35.0 70 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Ritesh Aggarwal,Radhika Gupta NaN 1.0 NaN 1.0 1.0 NaN NaN 2.0
336 3 Aretto 6 337 22-Jan-24 NaN 29-Jan-24 Nurturing The Spirit Of Entrepreneurship Rahul Dua Manufacturing Expandable Shoes For Kids https://wearetto.com/ 2020 1 1 <NA> <NA> 0 Middle Pune Maharashtra 700 60.0 57 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
343 3 WYLDCard 8 344 22-Jan-24 NaN 31-Jan-24 Entrepreneurial Innovation Rahul Dua Technology/Software Customer as Infulencer https://www.getwyld.in/ 2023 3 3 <NA> <NA> 0 Middle Mumbai Maharashtra <NA> NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Azhar Iqubal 1.0 NaN 1.0 NaN 1.0 1.0 NaN 1.0
344 3 upliance.ai 8 345 22-Jan-24 NaN 31-Jan-24 Entrepreneurial Innovation Rahul Dua Furnishing/Household Smart Cooker upliance.ai 2021 2 2 <NA> <NA> 0 Middle Bangalore Karnataka <NA> NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Azhar Iqubal 1.0 NaN 1.0 NaN 1.0 1.0 NaN 1.0
347 3 RooftopApp 9 348 22-Jan-24 NaN 1-Feb-24 Entrepreneurial Brilliance Rahul Dua Technology/Software Art Learning platform https://rooftopapp.com/ 2019 1 1 <NA> <NA> 0 Middle Mumbai Maharashtra <NA> NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Deepinder Goyal 1.0 1.0 1.0 1.0 NaN NaN NaN 1.0
352 3 EvaScalp 11 353 22-Jan-24 NaN 5-Feb-24 Disrupting The Status Quo Rahul Dua Medical/Health Post Chemo Scalp Cooling brand https://evascalpcooling.co.in/ 2020 1 1 <NA> <NA> 0 Middle Mumbai Maharashtra 59 10.0 85 <NA> ... 10.00 0.6000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 10.0 0.60 NaN Ritesh Aggarwal Ritesh Aggarwal 1.0 1.0 1.0 1.0 NaN NaN NaN 1.0
353 3 Elitty 11 354 22-Jan-24 NaN 5-Feb-24 Disrupting The Status Quo Rahul Dua Beauty/Fashion Teenage Make-up https://elittybeauty.com/ <NA> 2 <NA> 2 <NA> 0 Middle Gurgaon Haryana 113 11.0 36 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Ritesh Aggarwal 1.0 1.0 1.0 1.0 NaN NaN NaN 1.0
362 3 DecodeAge 14 363 22-Jan-24 NaN 8-Feb-24 Pitch Perfect Rahul Dua Medical/Health Age Longetivity Supplements https://decodeage.com/ 2021 3 3 <NA> <NA> 0 Middle Bangalore Karnataka 1100 NaN 70 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN 1.0 NaN NaN
366 3 AltCo 16 367 22-Jan-24 NaN 12-Feb-24 Innovation At Every Step Rahul Dua Food Plant based dairy products https://alt.company/ 2020 2 2 <NA> <NA> 0 Middle Bangalore Karnataka 1000 110.0 <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Ronnie Screwvala,Radhika Gupta NaN NaN 1.0 1.0 1.0 NaN NaN 2.0
377 3 ToffeeCoffeeRoasters 19 378 22-Jan-24 NaN 15-Feb-24 The Next Big Investment Rahul Dua Liquor/Beverages Coffee brand https://toffeecoffeeroasters.com/ 2019 2 1 1 <NA> 1 Middle Bangalore Karnataka 230 33.0 62 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 35.0 2.33 25.0 Ritesh Aggarwal Ritesh Aggarwal,Radhika Gupta NaN 1.0 NaN 1.0 1.0 NaN NaN 2.0
379 3 ORBO 20 380 22-Jan-24 NaN 16-Feb-24 Pioneering Change Rahul Dua Technology/Software AI-powered tools for beauty brands https://www.orbo.ai/ 2019 3 3 <NA> <NA> 0 Middle Mumbai Maharashtra <NA> 18.0 <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Azhar Iqubal NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0
384 3 D'chica 22 385 22-Jan-24 NaN 20-Feb-24 Impressive Numbers and High Stakes Rahul Dua Beauty/Fashion Innerwears for Teenage Girls https://www.dchica.in/ 2020 2 <NA> 2 <NA> 0 Middle Delhi Delhi 1030 120.0 <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN 1.0 NaN NaN
387 3 Artinci 23 388 22-Jan-24 NaN 21-Feb-24 Celebrating Entrepreneurial Breakthroughs Rahul Dua Food Zero Sugar Desserts https://www.artinci.com/ 2017 2 1 1 <NA> 1 Middle Bangalore Karnataka 440 33.0 62 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
396 3 Aroleap 26 397 22-Jan-24 NaN 26-Feb-24 Entrepreneurship On The Rise Rahul Dua Medical/Health Smart Home Gym https://www.aroleap.com/ 2020 3 3 <NA> <NA> 0 Middle Bangalore Karnataka 100 NaN <NA> <NA> ... NaN NaN NaN 25.00 1.250 NaN 25.00 1.2500 NaN NaN NaN NaN 25.0 1.25 NaN Azhar Iqubal Azhar Iqubal 1.0 NaN 1.0 NaN 1.0 1.0 NaN 1.0
408 3 AvataarSkincare 30 409 22-Jan-24 NaN 1-Mar-24 Startups Pursuing Investment Rahul Dua Beauty/Fashion Skincare Services https://avataarskin.com/ 2022 1 <NA> 1 <NA> 0 Middle Bhopal Madhya Pradesh <NA> 60.0 87 <NA> ... 17.50 0.2500 17.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN
421 3 Niblerzz 34 422 22-Jan-24 NaN 7-Mar-24 Visionary Brands Shine Bright Rahul Dua Food Sugar Free Candy https://niblerzz.com/ 2022 2 <NA> 2 <NA> 0 Middle Mumbai Maharashtra 16 5.5 <NA> <NA> ... 10.00 5.0000 40.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Azhar Iqubal NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0
422 3 Sorich 34 423 22-Jan-24 NaN 7-Mar-24 Visionary Brands Shine Bright Rahul Dua Food Guilt free Snacks https://sorichorganics.com/ <NA> 2 1 1 <NA> 1 Middle Delhi Delhi 706 55.0 48 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Azhar Iqubal NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0
424 3 CremeCastle 35 425 22-Jan-24 NaN 8-Mar-24 Inspiring Women Entrepreneurs Rahul Dua Food Customised Cakes and bakery products https://cremecastle.in/ 2015 2 1 1 <NA> 1 Middle Noida Uttar Pradesh 720 NaN 65 <NA> ... NaN NaN NaN NaN NaN NaN 60.00 2.5000 NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN 1.0 NaN NaN
432 3 Cup-ji 38 433 22-Jan-24 NaN 13-Mar-24 Thoughts And Innovations Rahul Dua Liquor/Beverages Flavoured Instant Tea in Cups https://cupji.com/ 2022 2 2 <NA> <NA> 0 Middle Mumbai Maharashtra 62 3.5 40 <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN 1.0 NaN NaN
433 3 AToddlerThing 38 434 22-Jan-24 NaN 13-Mar-24 Thoughts And Innovations Rahul Dua Beauty/Fashion Baby Clothing and Essentials https://www.atoddlerthing.com/ 2017 2 1 1 <NA> 0 Middle Coimbatore Tamil Nadu 478 73.0 52 <NA> ... NaN NaN NaN NaN NaN NaN 40.00 2.0000 40.0 NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 NaN 1.0 NaN NaN
436 3 iDreamCareer 39 437 22-Jan-24 NaN 14-Mar-24 Sustainability Careers And Spirits Rahul Dua Technology/Software Career Counseling Platform https://idreamcareer.com/ 2012 2 2 <NA> <NA> 0 Middle Delhi,Bangalore Delhi,Karnataka 840 NaN <NA> <NA> ... 30.00 0.5000 25.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN 30.0 0.50 25.0 Ritesh Aggarwal Ritesh Aggarwal NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0
437 3 RockPaperRum 39 438 22-Jan-24 NaN 14-Mar-24 Sustainability Careers And Spirits Rahul Dua Liquor/Beverages Innovative Indian Rum https://www.rockpaperrum.com/ 2022 1 1 <NA> <NA> 0 Middle Mumbai Maharashtra <NA> NaN <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Ritesh Aggarwal NaN 1.0 1.0 1.0 1.0 NaN NaN 1.0
439 3 Sukham 40 440 22-Jan-24 NaN 15-Mar-24 Shaping A Healthier Future Rahul Dua Medical/Health Holistic sexual male wellness https://www.sukham.life/ <NA> 4 3 1 <NA> 1 Middle Delhi Delhi 47 21.0 <NA> <NA> ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Deepinder Goyal 1.0 1.0 1.0 1.0 NaN NaN NaN 1.0

56 rows × 78 columns

In [108]:
# Top 15 Highest Yearly Revenue brands, in all seasons
print(shark_tank.groupby('Startup Name')['Yearly Revenue'].max().nlargest(15))

tmpdf = shark_tank.sort_values('Yearly Revenue', ascending=False)[0:15]
fig = px.bar(tmpdf, x="Startup Name", y='Yearly Revenue', color="Startup Name", template='simple_white', title="<b>Highest revenue (in lakhs) of participated startups, in all seasons</b>", text=tmpdf['Yearly Revenue'])
fig.show()
Startup Name
FrenchCrown          7200.0
Rubans               5100.0
Toyshine             4500.0
GuardianGears        2500.0
GunjanAppsStudios    2400.0
UnStop               1600.0
StyloBug             1400.0
RaisingSuperstars    1300.0
DesmondJi            1200.0
Eume                 1200.0
PlayBoxTV            1020.0
oyehappy             1005.0
Alpino               1000.0
BlueTea              1000.0
HammerLifestyle      1000.0
Name: Yearly Revenue, dtype: float64
In [88]:
# Top 10 Highest Yearly Revenue brands, in latest/current season (3rd season)
print(shark_tank_season3.groupby('Startup Name')['Yearly Revenue'].max().nlargest(10))

tmpdf = shark_tank_season3.sort_values('Yearly Revenue', ascending=False)[0:10]
fig = px.bar(tmpdf, x="Startup Name", y='Yearly Revenue', color="Startup Name", template='simple_white', title="<b>Highest revenue (in lakhs) of participated startups, in Season 3</b>", text=tmpdf['Yearly Revenue'])
fig.show()
Startup Name
Refit                  18700
NasherMiles             5700
YesMadam                5000
BaccaBucci              4700
LittleBox               3600
Zorko                   3000
AdilQadri               2070
UrbanSpace              2050
HonestHome              1400
UnclePetersPanCakes     1400
Name: Yearly Revenue, dtype: Int32
In [114]:
# Filter data for the 3rd season
season3_data = df[df['Season Number'] == 3]
In [116]:
# Top 15 highest Gross Margin brands, in all seasons
print(shark_tank.groupby('Startup Name')['Gross Margin'].max().nlargest(15))

tmpdf = shark_tank.sort_values('Gross Margin', ascending=False)[0:15]
fig = px.bar(tmpdf, x="Startup Name", y='Gross Margin', color="Startup Name", template='simple_white', title="<b>Highest Gross margin (in %) of the brands (in all seasons)</b>", text=tmpdf['Gross Margin'].map(int).map(str) + "%")
fig.show()
Startup Name
Poo-de-Cologne        150.0
Farda                 115.0
Cocofit                95.0
UnStop                 90.0
MidNightAngelsByPC     83.0
Auli                   80.0
LeafyAffair            80.0
Pflow                  80.0
ekatra                 80.0
oyehappy               80.0
CosIQ                  75.0
Dabble                 75.0
JaipurWatchCompany     75.0
TheaandSid             75.0
Bummer                 70.0
Name: Gross Margin, dtype: float64
In [117]:
# Top 15 highest Net Margin brands, in all seasons
print(shark_tank.groupby('Startup Name')['Net Margin'].max().nlargest(15))

tmpdf = shark_tank.sort_values('Net Margin', ascending=False)[0:15]
fig = px.bar(tmpdf, x="Startup Name", y='Net Margin', color="Startup Name", template='simple_white', title="<b>Highest Net margin (in %) of the brands</b>", text=tmpdf['Net Margin'].map(int).map(str) + "%")
fig.show()
Startup Name
Cakelicious           40.0
TwistingScoops        40.0
SharmaJiKiAata        38.0
DrCubes               35.0
Pabiben               35.0
VAPerfume             35.0
NishHair              30.0
UpThrust              30.0
ekatra                28.0
Tipayi                26.0
Flhexible             25.0
MeduLance             24.0
eyenic                21.0
Febris                20.0
MidNightAngelsByPC    20.0
Name: Net Margin, dtype: float64
In [118]:
# Word cloud based on Business Description of startups came in all seasons
text = " Shark Tank India ".join(cat for cat in shark_tank.loc[shark_tank['Business Description'].notnull()]['Business Description'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=2000, height=1500, stopwords=stop_words, background_color='salmon', colormap='Pastel1', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(20,20))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
In [92]:
# Word cloud based on Business Description, startups came in current/latest season (3rd season)
text = " Shark Tank India ".join(cat for cat in shark_tank_season3.loc[shark_tank_season3['Business Description'].notnull()]['Business Description'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=2000, height=1500, stopwords=stop_words, background_color='salmon', colormap='Pastel2', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(20,16))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
In [93]:
# Correlation matrix
shark_tank.corr(numeric_only=True).style.background_gradient(cmap = 'Blues')
Out[93]:
  Season Number Episode Number Pitch Number Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Yearly Revenue Monthly Sales Gross Margin Net Margin EBITDA SKUs Original Ask Amount Original Offered Equity Valuation Requested Received Offer Accepted Offer Total Deal Amount Total Deal Equity Total Deal Debt Debt Interest Deal Valuation Number of Sharks in Deal Royalty Deal Advisory Shares Equity Namita Investment Amount Namita Investment Equity Namita Debt Amount Vineeta Investment Amount Vineeta Investment Equity Vineeta Debt Amount Anupam Investment Amount Anupam Investment Equity Anupam Debt Amount Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present
Season Number 1.000000 0.165993 0.938991 0.212098 -0.073709 -0.019409 0.035600 nan -0.033453 0.161995 0.186585 0.038127 -0.171579 nan -0.152633 -0.048222 -0.222444 0.081853 0.061306 0.134256 0.150178 -0.432962 0.081566 0.011851 0.293433 -0.074752 nan nan 0.162246 -0.280535 0.228971 0.348674 -0.531534 -0.131312 0.222332 -0.376758 0.317895 0.083637 -0.290386 -0.027655 0.189545 -0.273924 0.480248 -0.096492 -0.194977 0.103882 nan nan nan 0.270924 -0.402261 0.268711 nan nan nan nan nan nan nan 0.257709
Episode Number 0.165993 1.000000 0.388181 -0.001709 0.028587 0.060830 0.040278 nan -0.020922 0.053446 0.014307 0.025498 0.224991 -0.100959 0.104549 -0.030911 -0.072443 0.036941 -0.017463 0.071186 -0.102776 -0.118867 -0.010375 0.189114 -0.018649 0.019458 nan -0.989457 -0.049936 -0.066545 -0.250550 -0.140468 -0.285217 -0.140762 -0.158157 -0.192655 -0.107538 -0.034936 -0.141009 -0.438680 -0.066868 0.016492 0.185670 -0.172148 -0.258796 0.946028 -0.266455 -0.182477 1.000000 -0.347703 -0.070913 0.167497 nan nan nan nan nan nan nan -0.126931
Pitch Number 0.938991 0.388181 1.000000 0.200920 -0.065776 -0.019511 0.003186 nan -0.026957 0.168618 0.176293 0.038012 -0.106616 -0.088741 -0.114034 -0.061585 -0.203165 0.075639 -0.025026 0.096432 0.105442 -0.430507 0.065766 0.110168 0.262281 -0.060060 nan -0.997631 0.124369 -0.269117 0.001822 0.277413 -0.593869 -0.176980 0.152515 -0.395067 0.233881 0.062384 -0.299713 -0.193498 0.167375 -0.256414 0.460870 -0.179683 -0.330110 0.510863 -0.261785 -0.163796 1.000000 0.173347 -0.449065 0.565519 nan nan nan nan nan nan nan 0.228354
Started in 0.212098 -0.001709 0.200920 1.000000 -0.082014 0.042050 0.065528 nan -0.180592 -0.117439 -0.061515 0.372268 0.258344 -0.089895 -0.334776 -0.103409 -0.042775 -0.099580 0.120736 0.143688 -0.024553 -0.031207 -0.024675 0.167881 -0.086094 0.125226 nan 0.796448 -0.201499 -0.257600 -0.269031 -0.201068 -0.079121 0.377058 0.077847 -0.013993 0.574076 -0.147026 -0.032074 0.014568 -0.284248 -0.117426 0.071980 -0.421349 0.035042 -0.544331 0.145770 -0.332778 1.000000 0.235303 0.150895 0.303579 nan nan nan nan nan nan nan 0.069559
Number of Presenters -0.073709 0.028587 -0.065776 -0.082014 1.000000 0.757998 0.283863 nan 0.176139 0.019579 -0.001270 -0.206100 0.066237 -0.299121 -0.024387 -0.053648 -0.141338 0.066529 0.012508 -0.054645 0.099664 -0.190931 0.152315 0.123101 0.128628 0.068547 nan -0.880812 -0.007147 -0.093339 -0.265797 0.148447 -0.190457 -0.279278 0.120184 -0.237157 -0.280335 0.076863 -0.002672 0.339361 0.024934 -0.275373 0.501527 -0.199916 -0.108593 0.722705 -0.078089 -0.278986 nan -0.036984 -0.230962 -0.952982 nan nan nan nan nan nan nan -0.038172
Male Presenters -0.019409 0.060830 -0.019511 0.042050 0.757998 1.000000 0.033014 nan -0.309805 0.046550 0.026806 -0.120141 0.143627 -0.336476 0.097058 -0.038795 -0.153961 0.066412 0.008411 0.051746 0.093616 -0.131314 0.250178 0.106095 0.191994 0.066619 nan nan -0.005148 -0.145149 0.251081 0.065770 -0.110525 0.040427 0.119344 -0.207503 0.113837 0.183658 0.025831 -0.059643 0.024584 -0.192138 0.382420 -0.111801 -0.297954 0.760469 -0.169740 -0.169891 1.000000 -0.179377 -0.241198 -0.372594 nan nan nan nan nan nan nan -0.089400
Female Presenters 0.035600 0.040278 0.003186 0.065528 0.283863 0.033014 1.000000 nan -0.111463 -0.134308 -0.042923 0.058179 0.123260 -0.606177 -0.169992 -0.063666 -0.001856 0.023272 0.046768 -0.122638 -0.188664 0.013347 -0.103112 -0.302962 -0.124493 -0.155053 nan nan -0.166104 -0.135065 -0.591373 0.004639 -0.084876 -0.382282 -0.068333 0.057210 nan 0.106950 0.338261 0.195316 -0.189834 0.009135 -0.628539 -0.157921 0.663184 -0.711328 nan nan nan -0.099306 0.105189 nan nan nan nan nan nan nan nan 0.047938
Transgender Presenters nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Couple Presenters -0.033453 -0.020922 -0.026957 -0.180592 0.176139 -0.309805 -0.111463 nan 1.000000 0.029558 -0.006296 -0.131371 -0.088358 0.079286 -0.077149 -0.019618 -0.026054 0.044130 -0.051843 -0.073623 0.081265 -0.047517 -0.128943 0.069248 0.017568 -0.024258 nan nan 0.158871 0.183871 -0.393045 0.225503 -0.022463 -0.364986 0.064985 -0.081840 -0.439941 0.040536 -0.081253 0.021341 0.066791 -0.023587 -0.131753 0.065055 -0.134876 -0.328502 0.311533 -0.147427 -1.000000 -0.019907 -0.203369 -0.359027 nan nan nan nan nan nan nan 0.021327
Yearly Revenue 0.161995 0.053446 0.168618 -0.117439 0.019579 0.046550 -0.134308 nan 0.029558 1.000000 0.960723 -0.196654 -0.099937 0.047336 0.635944 -0.012668 -0.241477 0.420723 0.089209 0.007592 0.426605 -0.180696 0.109087 0.119941 0.570696 0.238430 nan 1.000000 0.093676 -0.277898 -0.192972 0.394862 -0.299970 -0.242940 0.301259 -0.264331 0.245613 0.089555 -0.129570 -0.512487 0.015215 -0.200131 0.000000 0.224041 -0.329391 0.662975 0.649849 -0.291415 nan 0.156418 -0.372766 0.298133 nan nan nan nan nan nan nan 0.051283
Monthly Sales 0.186585 0.014307 0.176293 -0.061515 -0.001270 0.026806 -0.042923 nan -0.006296 0.960723 1.000000 -0.222777 -0.293741 0.410348 0.101299 -0.003653 -0.186564 0.329484 0.054820 -0.014604 0.397487 -0.148271 0.197484 0.441757 0.580422 0.041845 nan -1.000000 0.138890 -0.333172 0.313564 0.306777 -0.217983 0.331079 0.395109 -0.179823 0.318739 0.565563 -0.309680 -0.468403 0.327598 -0.144140 0.949158 0.247172 -0.271093 0.587076 0.151069 -0.460915 nan 0.608080 -0.335827 0.917171 nan nan nan nan nan nan nan 0.008203
Gross Margin 0.038127 0.025498 0.038012 0.372268 -0.206100 -0.120141 0.058179 nan -0.131371 -0.196654 -0.222777 1.000000 0.334639 0.363241 -0.843233 -0.150168 -0.030419 -0.138236 -0.015930 0.186509 -0.147143 0.030137 -0.036532 0.162457 -0.127319 0.114566 nan nan -0.068330 -0.004651 0.222146 0.055460 -0.092342 0.211237 -0.052256 -0.052607 0.533769 -0.279248 0.026779 -0.864464 -0.229211 0.077915 -0.057992 -0.604607 0.099229 -1.000000 -0.670061 0.707695 nan -0.355616 -0.346967 0.508576 nan nan nan nan nan nan nan 0.257626
Net Margin -0.171579 0.224991 -0.106616 0.258344 0.066237 0.143627 0.123260 nan -0.088358 -0.099937 -0.293741 0.334639 1.000000 -0.720577 -0.453014 -0.138014 0.460646 -0.205430 0.055656 0.212698 -0.341449 0.516857 0.466627 -0.893309 -0.280411 0.018103 nan nan -0.604944 -0.047809 nan -0.307421 0.306428 nan -0.157330 0.660418 nan 0.033958 0.438284 0.973684 -0.310531 0.326559 nan -0.529238 0.358334 -1.000000 nan nan nan -0.516589 0.767058 nan nan nan nan nan nan nan nan 0.175889
EBITDA nan -0.100959 -0.088741 -0.089895 -0.299121 -0.336476 -0.606177 nan 0.079286 0.047336 0.410348 0.363241 -0.720577 1.000000 -0.130973 0.252753 -0.092894 0.042547 0.093900 0.152896 0.400438 0.067488 0.793677 0.215274 0.046667 0.242409 nan nan -0.303043 -0.236433 nan 0.998137 0.999554 nan 0.265481 0.033219 nan 0.568731 -0.675184 1.000000 nan nan nan -0.086588 0.363713 nan nan nan nan 1.000000 1.000000 nan nan nan nan nan nan nan nan 0.666776
SKUs -0.152633 0.104549 -0.114034 -0.334776 -0.024387 0.097058 -0.169992 nan -0.077149 0.635944 0.101299 -0.843233 -0.453014 -0.130973 1.000000 0.025972 -0.270435 0.250590 0.164240 0.073754 -0.087194 -0.230059 -0.871817 nan 0.019151 -0.190829 nan nan -1.000000 1.000000 nan 0.812178 -0.500000 nan 0.071019 0.699011 nan -0.257537 -0.085186 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan -0.109575
Original Ask Amount -0.048222 -0.030911 -0.061585 -0.103409 -0.053648 -0.038795 -0.063666 nan -0.019618 -0.012668 -0.003653 -0.150168 -0.138014 0.252753 0.025972 1.000000 0.266994 0.614736 -0.076332 -0.231998 0.718948 -0.297513 0.875036 0.160046 0.744812 0.077581 nan -0.269565 0.485975 -0.251609 0.291903 0.549843 -0.266759 0.515546 0.510099 -0.209983 0.719931 0.464068 -0.238795 0.199305 0.587167 -0.210585 0.518195 0.531077 -0.425723 0.763852 0.385142 -0.179681 1.000000 0.360845 -0.168632 0.891364 nan nan nan nan nan nan nan -0.034174
Original Offered Equity -0.222444 -0.072443 -0.203165 -0.042775 -0.141338 -0.153961 -0.001856 nan -0.026054 -0.241477 -0.186564 -0.030419 0.460646 -0.092894 -0.270435 0.266994 1.000000 -0.159605 -0.173207 0.071146 -0.328453 0.686292 -0.191976 -0.406454 -0.404858 0.025618 nan 0.030373 -0.277727 0.632737 0.494519 -0.426339 0.620137 -0.152513 -0.310447 0.532555 -0.166422 -0.365176 0.334861 0.135962 -0.198120 0.560722 -0.637381 -0.544839 0.301131 -0.781745 -0.350304 0.766988 -1.000000 -0.237020 0.546484 0.187940 nan nan nan nan nan nan nan -0.189596
Valuation Requested 0.081853 0.036941 0.075639 -0.099580 0.066529 0.066412 0.023272 nan 0.044130 0.420723 0.329484 -0.138236 -0.205430 0.042547 0.250590 0.614736 -0.159605 1.000000 0.013093 -0.136789 0.550233 -0.359464 0.376454 0.186514 0.826239 0.116913 nan -0.209162 0.312662 -0.343628 -0.204553 0.475518 -0.378316 -0.087602 0.396726 -0.303209 0.502577 0.313034 -0.279927 -0.211592 0.343128 -0.245236 0.270917 0.342471 -0.308730 0.591304 0.356795 -0.344763 1.000000 0.187570 -0.345155 0.103562 nan nan nan nan nan nan nan 0.037627
Received Offer 0.061306 -0.017463 -0.025026 0.120736 0.012508 0.008411 0.046768 nan -0.051843 0.089209 0.054820 -0.015930 0.055656 0.093900 0.164240 -0.076332 -0.173207 0.013093 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.104706
Accepted Offer 0.134256 0.071186 0.096432 0.143688 -0.054645 0.051746 -0.122638 nan -0.073623 0.007592 -0.014604 0.186509 0.212698 0.152896 0.073754 -0.231998 0.071146 -0.136789 nan 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.067850
Total Deal Amount 0.150178 -0.102776 0.105442 -0.024553 0.099664 0.093616 -0.188664 nan 0.081265 0.426605 0.397487 -0.147143 -0.341449 0.400438 -0.087194 0.718948 -0.328453 0.550233 nan nan 1.000000 -0.208890 0.539594 0.200191 0.663985 0.327959 nan -0.030373 0.461891 -0.282010 0.044096 0.512598 -0.381410 0.283410 0.519919 -0.277993 0.569578 0.510879 -0.157542 -0.032224 0.615546 -0.292754 0.374517 0.497227 -0.336586 -0.329343 0.610433 -0.117275 -1.000000 0.661743 -0.022326 0.620285 nan nan nan nan nan nan nan 0.135177
Total Deal Equity -0.432962 -0.118867 -0.430507 -0.031207 -0.190931 -0.131314 0.013347 nan -0.047517 -0.180696 -0.148271 0.030137 0.516857 0.067488 -0.230059 -0.297513 0.686292 -0.359464 nan nan -0.208890 1.000000 -0.276474 -0.294494 -0.383184 0.001520 nan 0.762187 -0.207216 0.740784 0.246986 -0.347248 0.849360 0.149519 -0.188450 0.795484 -0.076059 -0.142621 0.761101 0.085786 -0.174186 0.911218 -0.465025 -0.360431 0.748191 -0.829397 -0.234626 0.885537 -1.000000 -0.079096 0.849757 0.322929 nan nan nan nan nan nan nan -0.189206
Total Deal Debt 0.081566 -0.010375 0.065766 -0.024675 0.152315 0.250178 -0.103112 nan -0.128943 0.109087 0.197484 -0.036532 0.466627 0.793677 -0.871817 0.875036 -0.191976 0.376454 nan nan 0.539594 -0.276474 1.000000 0.045565 0.515426 0.398541 nan nan 0.634659 -0.151591 0.542212 0.431945 -0.099005 0.720099 0.515652 0.008170 0.802924 0.255011 -0.389433 0.422350 0.544555 -0.448814 0.643727 -0.164013 -0.823649 0.984213 -1.000000 -1.000000 1.000000 0.772072 0.341584 0.964181 nan nan nan nan nan nan nan 0.213265
Debt Interest 0.011851 0.189114 0.110168 0.167881 0.123101 0.106095 -0.302962 nan 0.069248 0.119941 0.441757 0.162457 -0.893309 0.215274 nan 0.160046 -0.406454 0.186514 nan nan 0.200191 -0.294494 0.045565 1.000000 0.206331 0.136780 nan nan 0.352882 -0.360717 -0.115168 0.230848 0.347957 -0.004961 -0.445133 -0.692034 0.392911 0.512840 -0.881064 -0.207624 0.050443 -0.766907 -0.453990 -0.114708 -0.866025 0.976221 nan nan nan -0.217775 0.087682 -0.081987 nan nan nan nan nan nan nan 0.294880
Deal Valuation 0.293433 -0.018649 0.262281 -0.086094 0.128628 0.191994 -0.124493 nan 0.017568 0.570696 0.580422 -0.127319 -0.280411 0.046667 0.019151 0.744812 -0.404858 0.826239 nan nan 0.663985 -0.383184 0.515426 0.206331 1.000000 0.119974 nan -0.186359 0.363722 -0.348803 -0.025920 0.529455 -0.431773 -0.174686 0.384863 -0.357971 -0.233960 0.357411 -0.335226 -0.111052 0.643808 -0.253990 0.459398 0.403495 -0.331164 0.481815 0.695487 -0.422535 -1.000000 0.268676 -0.379049 -0.010826 nan nan nan nan nan nan nan 0.004660
Number of Sharks in Deal -0.074752 0.019458 -0.060060 0.125226 0.068547 0.066619 -0.155053 nan -0.024258 0.238430 0.041845 0.114566 0.018103 0.242409 -0.190829 0.077581 0.025618 0.116913 nan nan 0.327959 0.001520 0.398541 0.136780 0.119974 1.000000 nan 0.030373 -0.448819 -0.401752 -0.433988 -0.419515 -0.373144 -0.409094 -0.473367 -0.505823 -0.262826 -0.435428 -0.272468 -0.371674 -0.366745 -0.382562 0.032769 -0.394250 -0.358344 -0.722705 -0.430449 -0.096582 -1.000000 -0.296979 -0.327519 0.706672 nan nan nan nan nan nan nan 0.021358
Royalty Deal nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Advisory Shares Equity nan -0.989457 -0.997631 0.796448 -0.880812 nan nan nan nan 1.000000 -1.000000 nan nan nan nan -0.269565 0.030373 -0.209162 nan nan -0.030373 0.762187 nan nan -0.186359 0.030373 nan 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan -1.000000 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.880812
Namita Investment Amount 0.162246 -0.049936 0.124369 -0.201499 -0.007147 -0.005148 -0.166104 nan 0.158871 0.093676 0.138890 -0.068330 -0.604944 -0.303043 -1.000000 0.485975 -0.277727 0.312662 nan nan 0.461891 -0.207216 0.634659 0.352882 0.363722 -0.448819 nan nan 1.000000 0.149906 0.594739 1.000000 -0.483917 1.000000 1.000000 -0.535728 nan 1.000000 -0.341252 1.000000 0.906842 -0.422597 1.000000 1.000000 -0.570674 nan 1.000000 0.178222 nan 1.000000 -0.271580 nan nan nan nan nan nan nan nan nan
Namita Investment Equity -0.280535 -0.066545 -0.269117 -0.257600 -0.093339 -0.145149 -0.135065 nan 0.183871 -0.277898 -0.333172 -0.004651 -0.047809 -0.236433 1.000000 -0.251609 0.632737 -0.343628 nan nan -0.282010 0.740784 -0.151591 -0.360717 -0.348803 -0.401752 nan nan 0.149906 1.000000 0.438389 -0.483917 1.000000 -1.000000 -0.535728 1.000000 nan -0.341252 1.000000 -1.000000 -0.473162 0.997131 -0.628619 -0.570674 1.000000 nan 0.178222 1.000000 nan -0.271580 1.000000 nan nan nan nan nan nan nan nan nan
Namita Debt Amount 0.228971 -0.250550 0.001822 -0.269031 -0.265797 0.251081 -0.591373 nan -0.393045 -0.192972 0.313564 0.222146 nan nan nan 0.291903 0.494519 -0.204553 nan nan 0.044096 0.246986 0.542212 -0.115168 -0.025920 -0.433988 nan nan 0.594739 0.438389 1.000000 1.000000 -1.000000 1.000000 nan nan nan 1.000000 -1.000000 1.000000 1.000000 -0.628619 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Vineeta Investment Amount 0.348674 -0.140468 0.277413 -0.201068 0.148447 0.065770 0.004639 nan 0.225503 0.394862 0.306777 0.055460 -0.307421 0.998137 0.812178 0.549843 -0.426339 0.475518 nan nan 0.512598 -0.347248 0.431945 0.230848 0.529455 -0.419515 nan nan 1.000000 -0.483917 1.000000 1.000000 -0.103649 0.589324 1.000000 -0.219316 1.000000 1.000000 -0.373616 nan 0.833330 -0.292998 nan 1.000000 -0.641754 nan 1.000000 -0.047601 nan 0.862329 -0.273027 -1.000000 nan nan nan nan nan nan nan 0.209982
Vineeta Investment Equity -0.531534 -0.285217 -0.593869 -0.079121 -0.190457 -0.110525 -0.084876 nan -0.022463 -0.299970 -0.217983 -0.092342 0.306428 0.999554 -0.500000 -0.266759 0.620137 -0.378316 nan nan -0.381410 0.849360 -0.099005 0.347957 -0.431773 -0.373144 nan nan -0.483917 1.000000 -1.000000 -0.103649 1.000000 0.275393 -0.219316 1.000000 -1.000000 -0.373616 1.000000 nan -0.359624 0.998008 nan -0.641754 1.000000 nan -0.047601 1.000000 nan -0.360294 0.992607 nan nan nan nan nan nan nan nan -0.303400
Vineeta Debt Amount -0.131312 -0.140762 -0.176980 0.377058 -0.279278 0.040427 -0.382282 nan -0.364986 -0.242940 0.331079 0.211237 nan nan nan 0.515546 -0.152513 -0.087602 nan nan 0.283410 0.149519 0.720099 -0.004961 -0.174686 -0.409094 nan nan 1.000000 -1.000000 1.000000 0.589324 0.275393 1.000000 1.000000 -1.000000 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan -1.000000 nan 1.000000 nan nan nan nan nan nan nan nan
Anupam Investment Amount 0.222332 -0.158157 0.152515 0.077847 0.120184 0.119344 -0.068333 nan 0.064985 0.301259 0.395109 -0.052256 -0.157330 0.265481 0.071019 0.510099 -0.310447 0.396726 nan nan 0.519919 -0.188450 0.515652 -0.445133 0.384863 -0.473367 nan nan 1.000000 -0.535728 nan 1.000000 -0.219316 1.000000 1.000000 0.173510 0.710196 1.000000 -0.368718 nan 1.000000 -0.026016 nan 1.000000 -0.702232 nan 1.000000 -0.848930 nan 1.000000 0.023255 nan nan nan nan nan nan nan nan -0.092450
Anupam Investment Equity -0.376758 -0.192655 -0.395067 -0.013993 -0.237157 -0.207503 0.057210 nan -0.081840 -0.264331 -0.179823 -0.052607 0.660418 0.033219 0.699011 -0.209983 0.532555 -0.303209 nan nan -0.277993 0.795484 0.008170 -0.692034 -0.357971 -0.505823 nan nan -0.535728 1.000000 nan -0.219316 1.000000 -1.000000 0.173510 1.000000 0.018616 -0.368718 1.000000 nan -0.026016 1.000000 nan -0.702232 1.000000 nan -0.848930 1.000000 nan 0.023255 1.000000 nan nan nan nan nan nan nan nan -0.147575
Anupam Debt Amount 0.317895 -0.107538 0.233881 0.574076 -0.280335 0.113837 nan nan -0.439941 0.245613 0.318739 0.533769 nan nan nan 0.719931 -0.166422 0.502577 nan nan 0.569578 -0.076059 0.802924 0.392911 -0.233960 -0.262826 nan nan nan nan nan 1.000000 -1.000000 1.000000 0.710196 0.018616 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan -1.000000
Aman Investment Amount 0.083637 -0.034936 0.062384 -0.147026 0.076863 0.183658 0.106950 nan 0.040536 0.089555 0.565563 -0.279248 0.033958 0.568731 -0.257537 0.464068 -0.365176 0.313034 nan nan 0.510879 -0.142621 0.255011 0.512840 0.357411 -0.435428 nan nan 1.000000 -0.341252 1.000000 1.000000 -0.373616 nan 1.000000 -0.368718 nan 1.000000 0.205214 0.305398 0.941017 -0.359914 1.000000 1.000000 -0.285299 nan 1.000000 -0.221582 nan 0.796005 -0.035164 -0.638647 nan nan nan nan nan nan nan 0.218031
Aman Investment Equity -0.290386 -0.141009 -0.299713 -0.032074 -0.002672 0.025831 0.338261 nan -0.081253 -0.129570 -0.309680 0.026779 0.438284 -0.675184 -0.085186 -0.238795 0.334861 -0.279927 nan nan -0.157542 0.761101 -0.389433 -0.881064 -0.335226 -0.272468 nan nan -0.341252 1.000000 -1.000000 -0.373616 1.000000 nan -0.368718 1.000000 nan 0.205214 1.000000 0.106403 -0.396135 0.998493 -1.000000 -0.285299 1.000000 nan -0.221582 1.000000 nan 0.145482 0.871526 0.988411 nan nan nan nan nan nan nan 0.159303
Aman Debt Amount -0.027655 -0.438680 -0.193498 0.014568 0.339361 -0.059643 0.195316 nan 0.021341 -0.512487 -0.468403 -0.864464 0.973684 1.000000 nan 0.199305 0.135962 -0.211592 nan nan -0.032224 0.085786 0.422350 -0.207624 -0.111052 -0.371674 nan nan 1.000000 -1.000000 1.000000 nan nan nan nan nan nan 0.305398 0.106403 1.000000 1.000000 -1.000000 1.000000 nan nan nan nan nan nan 0.998645 0.822778 0.899521 nan nan nan nan nan nan nan -0.068380
Peyush Investment Amount 0.189545 -0.066868 0.167375 -0.284248 0.024934 0.024584 -0.189834 nan 0.066791 0.015215 0.327598 -0.229211 -0.310531 nan nan 0.587167 -0.198120 0.343128 nan nan 0.615546 -0.174186 0.544555 0.050443 0.643808 -0.366745 nan -1.000000 0.906842 -0.473162 1.000000 0.833330 -0.359624 nan 1.000000 -0.026016 nan 0.941017 -0.396135 1.000000 1.000000 -0.033575 0.402295 1.000000 -0.450754 nan 1.000000 0.599762 nan 0.859351 -0.094667 nan nan nan nan nan nan nan nan -0.000434
Peyush Investment Equity -0.273924 0.016492 -0.256414 -0.117426 -0.275373 -0.192138 0.009135 nan -0.023587 -0.200131 -0.144140 0.077915 0.326559 nan nan -0.210585 0.560722 -0.245236 nan nan -0.292754 0.911218 -0.448814 -0.766907 -0.253990 -0.382562 nan 1.000000 -0.422597 0.997131 -0.628619 -0.292998 0.998008 nan -0.026016 1.000000 nan -0.359914 0.998493 -1.000000 -0.033575 1.000000 -0.401912 -0.450754 1.000000 nan 0.599762 1.000000 nan -0.220911 0.991519 nan nan nan nan nan nan nan nan -0.242395
Peyush Debt Amount 0.480248 0.185670 0.460870 0.071980 0.501527 0.382420 -0.628539 nan -0.131753 0.000000 0.949158 -0.057992 nan nan nan 0.518195 -0.637381 0.270917 nan nan 0.374517 -0.465025 0.643727 -0.453990 0.459398 0.032769 nan nan 1.000000 -0.628619 1.000000 nan nan nan nan nan nan 1.000000 -1.000000 1.000000 0.402295 -0.401912 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Amit Investment Amount -0.096492 -0.172148 -0.179683 -0.421349 -0.199916 -0.111801 -0.157921 nan 0.065055 0.224041 0.247172 -0.604607 -0.529238 -0.086588 nan 0.531077 -0.544839 0.342471 nan nan 0.497227 -0.360431 -0.164013 -0.114708 0.403495 -0.394250 nan nan 1.000000 -0.570674 nan 1.000000 -0.641754 nan 1.000000 -0.702232 nan 1.000000 -0.285299 nan 1.000000 -0.450754 nan 1.000000 -0.014002 -0.066443 nan nan nan 1.000000 -0.962400 nan nan nan nan nan nan nan nan nan
Amit Investment Equity -0.194977 -0.258796 -0.330110 0.035042 -0.108593 -0.297954 0.663184 nan -0.134876 -0.329391 -0.271093 0.099229 0.358334 0.363713 nan -0.425723 0.301131 -0.308730 nan nan -0.336586 0.748191 -0.823649 -0.866025 -0.331164 -0.358344 nan nan -0.570674 1.000000 nan -0.641754 1.000000 nan -0.702232 1.000000 nan -0.285299 1.000000 nan -0.450754 1.000000 nan -0.014002 1.000000 -0.892073 nan nan nan -0.962400 1.000000 nan nan nan nan nan nan nan nan nan
Amit Debt Amount 0.103882 0.946028 0.510863 -0.544331 0.722705 0.760469 -0.711328 nan -0.328502 0.662975 0.587076 -1.000000 -1.000000 nan nan 0.763852 -0.781745 0.591304 nan nan -0.329343 -0.829397 0.984213 0.976221 0.481815 -0.722705 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan -0.066443 -0.892073 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Ashneer Investment Amount nan -0.266455 -0.261785 0.145770 -0.078089 -0.169740 nan nan 0.311533 0.649849 0.151069 -0.670061 nan nan nan 0.385142 -0.350304 0.356795 nan nan 0.610433 -0.234626 -1.000000 nan 0.695487 -0.430449 nan nan 1.000000 0.178222 nan 1.000000 -0.047601 nan 1.000000 -0.848930 nan 1.000000 -0.221582 nan 1.000000 0.599762 nan nan nan nan 1.000000 -0.039823 -1.000000 nan nan nan nan nan nan nan nan nan nan nan
Ashneer Investment Equity nan -0.182477 -0.163796 -0.332778 -0.278986 -0.169891 nan nan -0.147427 -0.291415 -0.460915 0.707695 nan nan nan -0.179681 0.766988 -0.344763 nan nan -0.117275 0.885537 -1.000000 nan -0.422535 -0.096582 nan nan 0.178222 1.000000 nan -0.047601 1.000000 nan -0.848930 1.000000 nan -0.221582 1.000000 nan 0.599762 1.000000 nan nan nan nan -0.039823 1.000000 -1.000000 nan nan nan nan nan nan nan nan nan nan nan
Ashneer Debt Amount nan 1.000000 1.000000 1.000000 nan 1.000000 nan nan -1.000000 nan nan nan nan nan nan 1.000000 -1.000000 1.000000 nan nan -1.000000 -1.000000 1.000000 nan -1.000000 -1.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan -1.000000 -1.000000 1.000000 nan nan nan nan nan nan nan nan nan nan nan
Guest Investment Amount 0.270924 -0.347703 0.173347 0.235303 -0.036984 -0.179377 -0.099306 nan -0.019907 0.156418 0.608080 -0.355616 -0.516589 1.000000 nan 0.360845 -0.237020 0.187570 nan nan 0.661743 -0.079096 0.772072 -0.217775 0.268676 -0.296979 nan nan 1.000000 -0.271580 nan 0.862329 -0.360294 -1.000000 1.000000 0.023255 nan 0.796005 0.145482 0.998645 0.859351 -0.220911 nan 1.000000 -0.962400 nan nan nan nan 1.000000 0.309836 0.809262 nan nan nan nan nan nan nan 0.233351
Guest Investment Equity -0.402261 -0.070913 -0.449065 0.150895 -0.230962 -0.241198 0.105189 nan -0.203369 -0.372766 -0.335827 -0.346967 0.767058 1.000000 nan -0.168632 0.546484 -0.345155 nan nan -0.022326 0.849757 0.341584 0.087682 -0.379049 -0.327519 nan nan -0.271580 1.000000 nan -0.273027 0.992607 nan 0.023255 1.000000 nan -0.035164 0.871526 0.822778 -0.094667 0.991519 nan -0.962400 1.000000 nan nan nan nan 0.309836 1.000000 0.503827 nan nan nan nan nan nan nan -0.143504
Guest Debt Amount 0.268711 0.167497 0.565519 0.303579 -0.952982 -0.372594 nan nan -0.359027 0.298133 0.917171 0.508576 nan nan nan 0.891364 0.187940 0.103562 nan nan 0.620285 0.322929 0.964181 -0.081987 -0.010826 0.706672 nan nan nan nan nan -1.000000 nan 1.000000 nan nan nan -0.638647 0.988411 0.899521 nan nan nan nan nan nan nan nan nan 0.809262 0.503827 1.000000 nan nan nan nan nan nan nan 0.641304
Namita Present nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Vineeta Present nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Anupam Present nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Aman Present nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Peyush Present nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Amit Present nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Ashneer Present nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Guest Present 0.257709 -0.126931 0.228354 0.069559 -0.038172 -0.089400 0.047938 nan 0.021327 0.051283 0.008203 0.257626 0.175889 0.666776 -0.109575 -0.034174 -0.189596 0.037627 0.104706 0.067850 0.135177 -0.189206 0.213265 0.294880 0.004660 0.021358 nan 0.880812 nan nan nan 0.209982 -0.303400 nan -0.092450 -0.147575 -1.000000 0.218031 0.159303 -0.068380 -0.000434 -0.242395 nan nan nan nan nan nan nan 0.233351 -0.143504 0.641304 nan nan nan nan nan nan nan 1.000000
In [94]:
print("numpy version: {}". format(np.__version__))
print("pandas version: {}". format(pd.__version__))
import matplotlib
print("matplotlib version: {}". format(matplotlib. __version__))
print("seaborn version: {}". format(sns.__version__))
import plotly
print("plotly version: {}". format(plotly.__version__))

# Current Python package versions
# numpy version: 1.26.4
# pandas version: 2.2.0
# matplotlib version: 3.7.5
# seaborn version: 0.12.2
# plotly version: 5.18.0
numpy version: 1.26.4
pandas version: 2.2.0
matplotlib version: 3.7.5
seaborn version: 0.12.2
plotly version: 5.18.0

Data set values verification (you can ignore below section)¶

below queries should not return any rows¶
In [95]:
shark_tank.loc[shark_tank['Number of Presenters'] != shark_tank['Male Presenters'].fillna(0) + shark_tank['Female Presenters'] + shark_tank['Transgender Presenters'].fillna(0)]
Out[95]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [96]:
shark_tank.loc[(shark_tank['Male Presenters'].isnull()) & (shark_tank['Couple Presenters'] == 1)]
Out[96]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [97]:
shark_tank.loc[(shark_tank['Female Presenters'].isnull()) & (shark_tank['Couple Presenters'] == 1)]
Out[97]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [98]:
shark_tank.loc[(shark_tank['Accepted Offer'] == 1) & (shark_tank['Total Deal Amount'].isnull())]
Out[98]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [99]:
shark_tank.loc[(shark_tank['Accepted Offer'] == 1) & (shark_tank['Number of Sharks in Deal'].isnull())]
Out[99]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [100]:
shark_tank.loc[(shark_tank['Accepted Offer'].isnull()) & (shark_tank['Number of Sharks in Deal'] >= 1)]
Out[100]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [101]:
shark_tank.loc[round(shark_tank['Total Deal Amount'].fillna(0),1) != round(shark_tank['Ashneer Investment Amount'].fillna(0) + shark_tank['Namita Investment Amount'].fillna(0) + shark_tank['Anupam Investment Amount'].fillna(0) + shark_tank['Vineeta Investment Amount'].fillna(0) + shark_tank['Aman Investment Amount'].fillna(0) + shark_tank['Peyush Investment Amount'].fillna(0) + shark_tank['Amit Investment Amount'].fillna(0) + shark_tank['Guest Investment Amount'].fillna(0), 1)]
Out[101]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [102]:
shark_tank.loc[round(shark_tank['Total Deal Equity'].fillna(0),1) != round(shark_tank['Ashneer Investment Equity'].fillna(0) + shark_tank['Namita Investment Equity'].fillna(0) + shark_tank['Anupam Investment Equity'].fillna(0) + shark_tank['Vineeta Investment Equity'].fillna(0) + shark_tank['Aman Investment Equity'].fillna(0) + shark_tank['Peyush Investment Equity'].fillna(0) + shark_tank['Amit Investment Equity'].fillna(0) + shark_tank['Guest Investment Equity'].fillna(0),1)]
Out[102]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [103]:
shark_tank.loc[round(shark_tank['Total Deal Debt'].fillna(0),1) != round(shark_tank['Ashneer Debt Amount'].fillna(0) + shark_tank['Namita Debt Amount'].fillna(0) + shark_tank['Anupam Debt Amount'].fillna(0) + shark_tank['Vineeta Debt Amount'].fillna(0) + shark_tank['Aman Debt Amount'].fillna(0) + shark_tank['Peyush Debt Amount'].fillna(0) + shark_tank['Amit Debt Amount'].fillna(0) + shark_tank['Guest Debt Amount'].fillna(0),1)]
Out[103]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [104]:
shark_tank.loc[(shark_tank['Received Offer'] == 1) & (shark_tank['Accepted Offer'].isnull())]
Out[104]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [105]:
shark_tank.loc[(shark_tank['Received Offer'] == 0) & (shark_tank['Accepted Offer'].notnull())]
Out[105]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [106]:
shark_tank.loc[(shark_tank['Number of Sharks in Deal'].fillna(0).round(0).astype(int) != shark_tank['Ashneer Investment Amount'].notnull().astype("int") + shark_tank['Namita Investment Amount'].notnull().astype("int") + shark_tank['Anupam Investment Amount'].notnull().astype("int") + shark_tank['Vineeta Investment Amount'].notnull().astype("int") + shark_tank['Aman Investment Amount'].notnull().astype("int") + shark_tank['Peyush Investment Amount'].notnull().astype("int") + shark_tank['Amit Investment Amount'].notnull().astype("int") + shark_tank['Guest Investment Amount'].notnull().astype("int")) & (shark_tank['Guest Present']<2) ]
Out[106]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [107]:
shark_tank.loc[(shark_tank['Couple Presenters'] != 0) & (shark_tank['Couple Presenters'] != 1)]
Out[107]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [108]:
shark_tank.loc[(shark_tank['Received Offer'] != 0) & (shark_tank['Received Offer'] != 1)]
Out[108]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [109]:
shark_tank.loc[(shark_tank['Accepted Offer'] != 0) & (shark_tank['Accepted Offer'] != 1)]
Out[109]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [110]:
shark_tank.loc[(shark_tank['Ashneer Investment Amount'].notnull()) & (shark_tank['Ashneer Present'] != 1)]
Out[110]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [111]:
shark_tank.loc[(shark_tank['Namita Investment Amount'].notnull()) & (shark_tank['Namita Present'] != 1)]
Out[111]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [112]:
shark_tank.loc[(shark_tank['Anupam Investment Amount'].notnull()) & (shark_tank['Anupam Present'] != 1)]
Out[112]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [113]:
shark_tank.loc[(shark_tank['Vineeta Investment Amount'].notnull()) & (shark_tank['Vineeta Present'] != 1)]
Out[113]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [114]:
shark_tank.loc[(shark_tank['Aman Investment Amount'].notnull()) & (shark_tank['Aman Present'] != 1)]
Out[114]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [115]:
shark_tank.loc[(shark_tank['Peyush Investment Amount'].notnull()) & (shark_tank['Peyush Present'] != 1)]
Out[115]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [116]:
shark_tank.loc[(shark_tank['Amit Investment Amount'].notnull()) & (shark_tank['Amit Present'] != 1)]
Out[116]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [117]:
shark_tank.loc[(shark_tank['Guest Investment Amount'].notnull()) & (shark_tank['Guest Present'].isnull())]
Out[117]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [118]:
shark_tank.loc[(shark_tank['Guest Investment Amount'].notnull()) & (shark_tank['Invested Guest Name'].isnull())]
Out[118]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [119]:
shark_tank.loc[(shark_tank['All Guest Names'].isnull()) & (shark_tank['Guest Present'].notnull())]
Out[119]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [120]:
shark_tank.loc[(shark_tank['Total Deal Debt'].isnull()) & (shark_tank['Debt Interest'].notnull())]
Out[120]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [121]:
shark_tank.loc[(shark_tank['Received Offer'] == 0) & (shark_tank['Deal Has Conditions'].notnull())]
Out[121]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [122]:
shark_tank.loc[(shark_tank['Accepted Offer'] == 0) & (shark_tank['Royalty Deal'].notnull())]
Out[122]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [123]:
shark_tank.loc[(shark_tank['Accepted Offer'] == 0) & (shark_tank['Advisory Shares Equity'].notnull())]
Out[123]:
Season Number Startup Name Episode Number Pitch Number Season Start Season End Original Air Date Episode Title Anchor Industry Business Description Company Website Started in Number of Presenters Male Presenters Female Presenters Transgender Presenters Couple Presenters Pitchers Average Age Pitchers City Pitchers State Yearly Revenue Monthly Sales Gross Margin Net Margin ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Amit Investment Amount Amit Investment Equity Amit Debt Amount Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Guest Investment Amount Guest Investment Equity Guest Debt Amount Invested Guest Name All Guest Names Namita Present Vineeta Present Anupam Present Aman Present Peyush Present Amit Present Ashneer Present Guest Present

0 rows × 78 columns

In [ ]:
 
In [ ]:
 
In [ ]: